Overview

Dataset statistics

Number of variables58
Number of observations122381
Missing cells2144215
Missing cells (%)30.2%
Total size in memory54.2 MiB
Average record size in memory464.0 B

Variable types

Text42
Numeric14
Unsupported2

Alerts

ROAD_NM_ZIP_NO has 30208 (24.7%) missing valuesMissing
RDNMADR_ONE_NM has 12205 (10.0%) missing valuesMissing
RDNMADR_TWO_NM has 98199 (80.2%) missing valuesMissing
ZIP_NO_VALUE has 28078 (22.9%) missing valuesMissing
FCLTY_ADDR_ONE_NM has 19552 (16.0%) missing valuesMissing
FCLTY_ADDR_TWO_NM has 92372 (75.5%) missing valuesMissing
FCLTY_LO has 6513 (5.3%) missing valuesMissing
FCLTY_LA has 6513 (5.3%) missing valuesMissing
FCLTY_TEL_NO has 56103 (45.8%) missing valuesMissing
FCLTY_HMPG_URL has 118425 (96.8%) missing valuesMissing
FCLTY_MANAGE_CTPRVN_CD has 26464 (21.6%) missing valuesMissing
FCLTY_MANAGE_CTPRVN_NM has 27697 (22.6%) missing valuesMissing
FCLTY_MANAGE_SIGNGU_CD has 26957 (22.0%) missing valuesMissing
FCLTY_MANAGE_SIGNGU_NM has 28916 (23.6%) missing valuesMissing
FCLTY_MANAGE_EMD_CD has 55932 (45.7%) missing valuesMissing
FCLTY_MANAGE_EMD_NM has 56929 (46.5%) missing valuesMissing
FCLTY_MANAGE_LI_CD has 122381 (100.0%) missing valuesMissing
FCLTY_MANAGE_LI_NM has 122381 (100.0%) missing valuesMissing
FCLTY_OPER_STLE_VALUE has 62662 (51.2%) missing valuesMissing
POSESN_MBY_CD has 82451 (67.4%) missing valuesMissing
POSESN_MBY_NM has 82537 (67.4%) missing valuesMissing
POSESN_MBY_CTPRVN_CD has 45539 (37.2%) missing valuesMissing
POSESN_MBY_CTPRVN_NM has 45539 (37.2%) missing valuesMissing
POSESN_MBY_SIGNGU_CD has 45539 (37.2%) missing valuesMissing
POSESN_MBY_SIGNGU_NM has 46304 (37.8%) missing valuesMissing
RSPNSBLTY_NM has 98918 (80.8%) missing valuesMissing
RSPNSBLTY_TEL_NO has 74812 (61.1%) missing valuesMissing
NDOR_SDIV_NM has 47164 (38.5%) missing valuesMissing
ADTM_CO has 120904 (98.8%) missing valuesMissing
ACMD_NMPR_CO has 120617 (98.6%) missing valuesMissing
FCLTY_AR_CO has 38185 (31.2%) missing valuesMissing
LVLH_GMNSM_NM has 121164 (99.0%) missing valuesMissing
UTILIIZA_GRP_NM has 121533 (99.3%) missing valuesMissing
DATA_ORIGIN_FLAG_CD has 53075 (43.4%) missing valuesMissing
POSESN_MBY_CD is highly skewed (γ1 = 26.44124885)Skewed
FCLTY_AR_CO is highly skewed (γ1 = 290.16547)Skewed
FCLTY_MANAGE_LI_CD is an unsupported type, check if it needs cleaning or further analysisUnsupported
FCLTY_MANAGE_LI_NM is an unsupported type, check if it needs cleaning or further analysisUnsupported
FCLTY_LO has 2363 (1.9%) zerosZeros
FCLTY_LA has 2363 (1.9%) zerosZeros
FCLTY_AR_CO has 2272 (1.9%) zerosZeros

Reproduction

Analysis started2023-07-10 16:16:47.346902
Analysis finished2023-07-10 16:16:53.122261
Duration5.78 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

FCLTY_NM
Text

Distinct91926
Distinct (%)75.1%
Missing0
Missing (%)0.0%
Memory size956.2 KiB
2023-07-11T01:16:53.525821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length61
Median length42
Mean length8.294547356
Min length1

Characters and Unicode

Total characters1015095
Distinct characters1292
Distinct categories16 ?
Distinct scripts5 ?
Distinct blocks14 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique80544 ?
Unique (%)65.8%

Sample

1st row(외립석입구)
2nd row(주)한샘레포츠타운(공단점)
3rd row(자산경로당)
4th row안산 힘찬 태권도
5th row뽀록 당구장
ValueCountFrequency (%)
당구장 3320
 
1.7%
동네체육시설 2514
 
1.3%
당구클럽 2372
 
1.2%
체육시설 2270
 
1.2%
태권도장 2160
 
1.1%
태권도 1646
 
0.9%
휘트니스 1251
 
0.7%
야외운동기구 1249
 
0.7%
골프 1047
 
0.6%
용인대 961
 
0.5%
Other values (82654) 171117
90.1%
2023-07-11T01:16:54.087757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
67654
 
6.7%
장 39770
 
3.9%
구 34899
 
3.4%
스 32263
 
3.2%
당 28112
 
2.8%
도 18275
 
1.8%
프 18127
 
1.8%
골 16470
 
1.6%
체 15761
 
1.6%
이 15441
 
1.5%
Other values (1282) 728323
71.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 857337
84.5%
Space Separator 67654
 
6.7%
Uppercase Letter 36328
 
3.6%
Decimal Number 20709
 
2.0%
Lowercase Letter 11397
 
1.1%
Close Punctuation 8550
 
0.8%
Open Punctuation 8460
 
0.8%
Other Punctuation 2756
 
0.3%
Dash Punctuation 1619
 
0.2%
Math Symbol 121
 
< 0.1%
Other values (6) 164
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 39770
 
4.6%
구 34899
 
4.1%
스 32263
 
3.8%
당 28112
 
3.3%
도 18275
 
2.1%
프 18127
 
2.1%
골 16470
 
1.9%
체 15761
 
1.8%
이 15441
 
1.8%
동 15124
 
1.8%
Other values (1169) 623095
72.7%
Lowercase Letter
ValueCountFrequency (%)
e 1153
 
10.1%
i 1023
 
9.0%
o 983
 
8.6%
s 898
 
7.9%
n 765
 
6.7%
t 710
 
6.2%
a 702
 
6.2%
l 687
 
6.0%
r 617
 
5.4%
m 537
 
4.7%
Other values (17) 3322
29.1%
Uppercase Letter
ValueCountFrequency (%)
S 3308
 
9.1%
G 2990
 
8.2%
M 2830
 
7.8%
T 2708
 
7.5%
C 2035
 
5.6%
P 1972
 
5.4%
B 1950
 
5.4%
A 1894
 
5.2%
K 1723
 
4.7%
Y 1623
 
4.5%
Other values (16) 13295
36.6%
Other Punctuation
ValueCountFrequency (%)
. 1167
42.3%
& 865
31.4%
, 265
 
9.6%
' 145
 
5.3%
· 70
 
2.5%
: 52
 
1.9%
/ 47
 
1.7%
？ 38
 
1.4%
@ 34
 
1.2%
# 22
 
0.8%
Other values (9) 51
 
1.9%
Decimal Number
ValueCountFrequency (%)
1 5466
26.4%
2 5397
26.1%
3 2377
11.5%
4 1567
 
7.6%
0 1531
 
7.4%
5 1152
 
5.6%
7 861
 
4.2%
6 809
 
3.9%
8 790
 
3.8%
9 759
 
3.7%
Math Symbol
ValueCountFrequency (%)
~ 64
52.9%
+ 48
39.7%
> 3
 
2.5%
< 3
 
2.5%
＋ 1
 
0.8%
= 1
 
0.8%
→ 1
 
0.8%
Other Symbol
ValueCountFrequency (%)
㈜ 78
90.7%
� 2
 
2.3%
℃ 2
 
2.3%
ⓝ 2
 
2.3%
㎡ 2
 
2.3%
Letter Number
ValueCountFrequency (%)
Ⅱ 28
73.7%
Ⅲ 5
 
13.2%
Ⅰ 4
 
10.5%
Ⅳ 1
 
2.6%
Other Number
ValueCountFrequency (%)
② 3
30.0%
₂ 3
30.0%
③ 2
20.0%
① 2
20.0%
Modifier Symbol
ValueCountFrequency (%)
` 3
60.0%
´ 1
 
20.0%
˚ 1
 
20.0%
Close Punctuation
ValueCountFrequency (%)
) 8450
98.8%
] 100
 
1.2%
Open Punctuation
ValueCountFrequency (%)
( 8360
98.8%
[ 100
 
1.2%
Space Separator
ValueCountFrequency (%)
67654
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1619
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 24
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 857345
84.5%
Common 109917
 
10.8%
Latin 47763
 
4.7%
Han 69
 
< 0.1%
Greek 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 39770
 
4.6%
구 34899
 
4.1%
스 32263
 
3.8%
당 28112
 
3.3%
도 18275
 
2.1%
프 18127
 
2.1%
골 16470
 
1.9%
체 15761
 
1.8%
이 15441
 
1.8%
동 15124
 
1.8%
Other values (1136) 623103
72.7%
Latin
ValueCountFrequency (%)
S 3308
 
6.9%
G 2990
 
6.3%
M 2830
 
5.9%
T 2708
 
5.7%
C 2035
 
4.3%
P 1972
 
4.1%
B 1950
 
4.1%
A 1894
 
4.0%
K 1723
 
3.6%
Y 1623
 
3.4%
Other values (47) 24730
51.8%
Common
ValueCountFrequency (%)
67654
61.6%
) 8450
 
7.7%
( 8360
 
7.6%
1 5466
 
5.0%
2 5397
 
4.9%
3 2377
 
2.2%
- 1619
 
1.5%
4 1567
 
1.4%
0 1531
 
1.4%
. 1167
 
1.1%
Other values (45) 6329
 
5.8%
Han
ValueCountFrequency (%)
孝 17
24.6%
秀 9
 
13.0%
美 5
 
7.2%
道 3
 
4.3%
休 2
 
2.9%
樂 2
 
2.9%
水 2
 
2.9%
愛 2
 
2.9%
成 2
 
2.9%
武 2
 
2.9%
Other values (23) 23
33.3%
Greek
ValueCountFrequency (%)
α 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 857255
84.5%
ASCII 157483
 
15.5%
None 219
 
< 0.1%
CJK 65
 
< 0.1%
Number Forms 38
 
< 0.1%
Compat Jamo 12
 
< 0.1%
Enclosed Alphanum 9
 
< 0.1%
CJK Compat Ideographs 4
 
< 0.1%
Specials 2
 
< 0.1%
Letterlike Symbols 2
 
< 0.1%
Other values (4) 6
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
67654
43.0%
) 8450
 
5.4%
( 8360
 
5.3%
1 5466
 
3.5%
2 5397
 
3.4%
S 3308
 
2.1%
G 2990
 
1.9%
M 2830
 
1.8%
T 2708
 
1.7%
3 2377
 
1.5%
Other values (77) 47943
30.4%
Hangul
ValueCountFrequency (%)
장 39770
 
4.6%
구 34899
 
4.1%
스 32263
 
3.8%
당 28112
 
3.3%
도 18275
 
2.1%
프 18127
 
2.1%
골 16470
 
1.9%
체 15761
 
1.8%
이 15441
 
1.8%
동 15124
 
1.8%
Other values (1130) 623013
72.7%
None
ValueCountFrequency (%)
㈜ 78
35.6%
· 70
32.0%
？ 38
17.4%
＆ 19
 
8.7%
＠ 5
 
2.3%
₂ 3
 
1.4%
¡ 1
 
0.5%
． 1
 
0.5%
´ 1
 
0.5%
＋ 1
 
0.5%
Other values (2) 2
 
0.9%
Number Forms
ValueCountFrequency (%)
Ⅱ 28
73.7%
Ⅲ 5
 
13.2%
Ⅰ 4
 
10.5%
Ⅳ 1
 
2.6%
CJK
ValueCountFrequency (%)
孝 17
26.2%
秀 9
13.8%
美 5
 
7.7%
道 3
 
4.6%
休 2
 
3.1%
水 2
 
3.1%
愛 2
 
3.1%
成 2
 
3.1%
武 2
 
3.1%
帥 1
 
1.5%
Other values (20) 20
30.8%
Compat Jamo
ValueCountFrequency (%)
ㆍ 5
41.7%
ㅋ 4
33.3%
ㅠ 1
 
8.3%
ㅡ 1
 
8.3%
ㄹ 1
 
8.3%
Enclosed Alphanum
ValueCountFrequency (%)
② 3
33.3%
③ 2
22.2%
① 2
22.2%
ⓝ 2
22.2%
CJK Compat Ideographs
ValueCountFrequency (%)
樂 2
50.0%
李 1
25.0%
龍 1
25.0%
Specials
ValueCountFrequency (%)
� 2
100.0%
Letterlike Symbols
ValueCountFrequency (%)
℃ 2
100.0%
CJK Compat
ValueCountFrequency (%)
㎡ 2
100.0%
Punctuation
ValueCountFrequency (%)
’ 1
50.0%
′ 1
50.0%
Modifier Letters
ValueCountFrequency (%)
˚ 1
100.0%
Arrows
ValueCountFrequency (%)
→ 1
100.0%

FCLTY_SDIV_CD
Text

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size956.2 KiB
2023-07-11T01:16:54.159210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters122381
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowP
2nd rowN
3rd rowP
4th rowN
5th rowN
ValueCountFrequency (%)
n 90242
73.7%
p 31570
 
25.8%
r 569
 
0.5%
2023-07-11T01:16:54.301878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 90242
73.7%
P 31570
 
25.8%
R 569
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 122381
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 90242
73.7%
P 31570
 
25.8%
R 569
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 122381
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 90242
73.7%
P 31570
 
25.8%
R 569
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 122381
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 90242
73.7%
P 31570
 
25.8%
R 569
 
0.5%

FCLTY_FLAG_NM
Text

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size956.2 KiB
2023-07-11T01:16:54.388760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters244762
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row공공
2nd row신고
3rd row공공
4th row신고
5th row신고
ValueCountFrequency (%)
신고 90242
73.7%
공공 31570
 
25.8%
등록 569
 
0.5%
2023-07-11T01:16:54.553260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
신 90242
36.9%
고 90242
36.9%
공 63140
25.8%
등 569
 
0.2%
록 569
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 244762
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
신 90242
36.9%
고 90242
36.9%
공 63140
25.8%
등 569
 
0.2%
록 569
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 244762
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
신 90242
36.9%
고 90242
36.9%
공 63140
25.8%
등 569
 
0.2%
록 569
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 244762
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
신 90242
36.9%
고 90242
36.9%
공 63140
25.8%
등 569
 
0.2%
록 569
 
0.2%

INDUTY_CD
Text

Distinct45
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size956.2 KiB
2023-07-11T01:16:54.720987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters367143
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowP08
2nd rowN10
3rd rowP08
4th rowN08
5th rowN11
ValueCountFrequency (%)
n11 28915
23.6%
p08 22716
18.6%
n08 20680
16.9%
n10 16363
13.4%
n09 13950
11.4%
n16 4496
 
3.7%
n17 2033
 
1.7%
p10 1873
 
1.5%
p22 1492
 
1.2%
p09 1261
 
1.0%
Other values (35) 8602
 
7.0%
2023-07-11T01:16:54.987485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 90242
24.6%
1 87000
23.7%
0 82307
22.4%
8 43632
11.9%
P 31570
 
8.6%
9 15227
 
4.1%
6 5899
 
1.6%
2 4803
 
1.3%
7 3428
 
0.9%
4 1866
 
0.5%
Other values (3) 1169
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 244762
66.7%
Uppercase Letter 122381
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 87000
35.5%
0 82307
33.6%
8 43632
17.8%
9 15227
 
6.2%
6 5899
 
2.4%
2 4803
 
2.0%
7 3428
 
1.4%
4 1866
 
0.8%
5 336
 
0.1%
3 264
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
N 90242
73.7%
P 31570
 
25.8%
R 569
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 244762
66.7%
Latin 122381
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1 87000
35.5%
0 82307
33.6%
8 43632
17.8%
9 15227
 
6.2%
6 5899
 
2.4%
2 4803
 
2.0%
7 3428
 
1.4%
4 1866
 
0.8%
5 336
 
0.1%
3 264
 
0.1%
Latin
ValueCountFrequency (%)
N 90242
73.7%
P 31570
 
25.8%
R 569
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 367143
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 90242
24.6%
1 87000
23.7%
0 82307
22.4%
8 43632
11.9%
P 31570
 
8.6%
9 15227
 
4.1%
6 5899
 
1.6%
2 4803
 
1.3%
7 3428
 
0.9%
4 1866
 
0.5%
Other values (3) 1169
 
0.3%

INDUTY_NM
Text

Distinct44
Distinct (%)< 0.1%
Missing7
Missing (%)< 0.1%
Memory size956.2 KiB
2023-07-11T01:16:55.167348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length13
Median length10
Mean length5.154910357
Min length3

Characters and Unicode

Total characters630827
Distinct characters79
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row간이운동장
2nd row체력단련장업
3rd row간이운동장
4th row체육도장업
5th row당구장업
ValueCountFrequency (%)
당구장업 28915
22.8%
간이운동장 22716
17.9%
체육도장업 20680
16.3%
체력단련장업 16361
12.9%
골프연습장업 13950
11.0%
가상체험 4496
 
3.5%
체육시설업 4496
 
3.5%
체육교습업 2033
 
1.6%
전천후게이트볼장 1872
 
1.5%
기타시설 1491
 
1.2%
Other values (35) 9860
 
7.8%
2023-07-11T01:16:55.392686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 111530
17.7%
업 90814
14.4%
체 49911
 
7.9%
구 30469
 
4.8%
육 29283
 
4.6%
당 28915
 
4.6%
이 24802
 
3.9%
동 22721
 
3.6%
운 22716
 
3.6%
간 22716
 
3.6%
Other values (69) 196950
31.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 626191
99.3%
Space Separator 4496
 
0.7%
Open Punctuation 70
 
< 0.1%
Close Punctuation 70
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 111530
17.8%
업 90814
14.5%
체 49911
 
8.0%
구 30469
 
4.9%
육 29283
 
4.7%
당 28915
 
4.6%
이 24802
 
4.0%
동 22721
 
3.6%
운 22716
 
3.6%
간 22716
 
3.6%
Other values (66) 192314
30.7%
Space Separator
ValueCountFrequency (%)
4496
100.0%
Open Punctuation
ValueCountFrequency (%)
( 70
100.0%
Close Punctuation
ValueCountFrequency (%)
) 70
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 626191
99.3%
Common 4636
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 111530
17.8%
업 90814
14.5%
체 49911
 
8.0%
구 30469
 
4.9%
육 29283
 
4.7%
당 28915
 
4.6%
이 24802
 
4.0%
동 22721
 
3.6%
운 22716
 
3.6%
간 22716
 
3.6%
Other values (66) 192314
30.7%
Common
ValueCountFrequency (%)
4496
97.0%
( 70
 
1.5%
) 70
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 626191
99.3%
ASCII 4636
 
0.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 111530
17.8%
업 90814
14.5%
체 49911
 
8.0%
구 30469
 
4.9%
육 29283
 
4.7%
당 28915
 
4.6%
이 24802
 
4.0%
동 22721
 
3.6%
운 22716
 
3.6%
간 22716
 
3.6%
Other values (66) 192314
30.7%
ASCII
ValueCountFrequency (%)
4496
97.0%
( 70
 
1.5%
) 70
 
1.5%

FCLTY_TY_CD
Text

Distinct67
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size956.2 KiB
2023-07-11T01:16:55.587533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters611905
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowP0801
2nd rowN1001
3rd rowP0801
4th rowN0805
5th rowN1101
ValueCountFrequency (%)
n1101 28917
23.6%
p0801 22716
18.6%
n1001 16361
13.4%
n0805 14939
12.2%
n0903 6338
 
5.2%
n0901 5886
 
4.8%
n1601 4169
 
3.4%
n0801 2247
 
1.8%
p1001 1872
 
1.5%
n0902 1726
 
1.4%
Other values (57) 17210
14.1%
2023-07-11T01:16:55.892161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 204685
33.5%
1 179737
29.4%
N 90242
14.7%
8 44467
 
7.3%
P 31570
 
5.2%
9 15444
 
2.5%
5 15294
 
2.5%
3 8275
 
1.4%
2 7303
 
1.2%
6 6240
 
1.0%
Other values (3) 8648
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 489524
80.0%
Uppercase Letter 122381
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 204685
41.8%
1 179737
36.7%
8 44467
 
9.1%
9 15444
 
3.2%
5 15294
 
3.1%
3 8275
 
1.7%
2 7303
 
1.5%
6 6240
 
1.3%
7 5145
 
1.1%
4 2934
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
N 90242
73.7%
P 31570
 
25.8%
R 569
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 489524
80.0%
Latin 122381
 
20.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 204685
41.8%
1 179737
36.7%
8 44467
 
9.1%
9 15444
 
3.2%
5 15294
 
3.1%
3 8275
 
1.7%
2 7303
 
1.5%
6 6240
 
1.3%
7 5145
 
1.1%
4 2934
 
0.6%
Latin
ValueCountFrequency (%)
N 90242
73.7%
P 31570
 
25.8%
R 569
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 611905
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 204685
33.5%
1 179737
29.4%
N 90242
14.7%
8 44467
 
7.3%
P 31570
 
5.2%
9 15444
 
2.5%
5 15294
 
2.5%
3 8275
 
1.4%
2 7303
 
1.2%
6 6240
 
1.0%
Other values (3) 8648
 
1.4%

FCLTY_TY_NM
Text

Distinct58
Distinct (%)< 0.1%
Missing7
Missing (%)< 0.1%
Memory size956.2 KiB
2023-07-11T01:16:56.088852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length13
Median length8
Mean length3.644328044
Min length2

Characters and Unicode

Total characters445971
Distinct characters106
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row간이운동장
2nd row체력단련장
3rd row간이운동장
4th row태권도
5th row당구장
ValueCountFrequency (%)
당구장 28915
23.6%
간이운동장 22716
18.6%
체력단련장 16361
13.4%
태권도 14939
12.2%
실내 6819
 
5.6%
스크린 6338
 
5.2%
골프 4169
 
3.4%
권투 2247
 
1.8%
실외 1976
 
1.6%
전천후게이트볼장 1872
 
1.5%
Other values (48) 16022
13.1%
2023-07-11T01:16:56.329603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 75711
17.0%
구 32502
 
7.3%
당 28915
 
6.5%
이 24846
 
5.6%
동 22721
 
5.1%
간 22716
 
5.1%
운 22716
 
5.1%
도 19391
 
4.3%
체 18206
 
4.1%
권 17186
 
3.9%
Other values (96) 161061
36.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 445831
> 99.9%
Close Punctuation 70
 
< 0.1%
Open Punctuation 70
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 75711
17.0%
구 32502
 
7.3%
당 28915
 
6.5%
이 24846
 
5.6%
동 22721
 
5.1%
간 22716
 
5.1%
운 22716
 
5.1%
도 19391
 
4.3%
체 18206
 
4.1%
권 17186
 
3.9%
Other values (94) 160921
36.1%
Close Punctuation
ValueCountFrequency (%)
) 70
100.0%
Open Punctuation
ValueCountFrequency (%)
( 70
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 445831
> 99.9%
Common 140
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 75711
17.0%
구 32502
 
7.3%
당 28915
 
6.5%
이 24846
 
5.6%
동 22721
 
5.1%
간 22716
 
5.1%
운 22716
 
5.1%
도 19391
 
4.3%
체 18206
 
4.1%
권 17186
 
3.9%
Other values (94) 160921
36.1%
Common
ValueCountFrequency (%)
) 70
50.0%
( 70
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 445831
> 99.9%
ASCII 140
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 75711
17.0%
구 32502
 
7.3%
당 28915
 
6.5%
이 24846
 
5.6%
동 22721
 
5.1%
간 22716
 
5.1%
운 22716
 
5.1%
도 19391
 
4.3%
체 18206
 
4.1%
권 17186
 
3.9%
Other values (94) 160921
36.1%
ASCII
ValueCountFrequency (%)
) 70
50.0%
( 70
50.0%

FCLTY_STATE_VALUE
Text

Distinct2
Distinct (%)< 0.1%
Missing282
Missing (%)0.2%
Memory size956.2 KiB
2023-07-11T01:16:56.427677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.612216316
Min length2

Characters and Unicode

Total characters441048
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row정상운영
2nd row정상운영
3rd row정상운영
4th row정상운영
5th row폐업
ValueCountFrequency (%)
정상운영 98425
80.6%
폐업 23674
 
19.4%
2023-07-11T01:16:56.594737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
정 98425
22.3%
상 98425
22.3%
운 98425
22.3%
영 98425
22.3%
폐 23674
 
5.4%
업 23674
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 441048
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
정 98425
22.3%
상 98425
22.3%
운 98425
22.3%
영 98425
22.3%
폐 23674
 
5.4%
업 23674
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 441048
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
정 98425
22.3%
상 98425
22.3%
운 98425
22.3%
영 98425
22.3%
폐 23674
 
5.4%
업 23674
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 441048
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
정 98425
22.3%
상 98425
22.3%
운 98425
22.3%
영 98425
22.3%
폐 23674
 
5.4%
업 23674
 
5.4%

ROAD_NM_ZIP_NO
Real number (ℝ)

MISSING 

Distinct26339
Distinct (%)28.6%
Missing30208
Missing (%)24.7%
Infinite0
Infinite (%)0.0%
Mean123630.8424
Minimum1000
Maximum799803
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size956.2 KiB
2023-07-11T01:16:56.683083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile5242
Q115019
median36167
Q358952
95-th percentile637805
Maximum799803
Range798803
Interquartile range (IQR)43933

Descriptive statistics

Standard deviation203238.7403
Coefficient of variation (CV)1.643916165
Kurtosis2.12683859
Mean123630.8424
Median Absolute Deviation (MAD)21619
Skewness1.902758442
Sum1.139542564 × 1010
Variance4.130598557 × 1010
MonotonicityNot monotonic
2023-07-11T01:16:56.781934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46726 99
 
0.1%
10071 82
 
0.1%
31156 78
 
0.1%
15010 68
 
0.1%
30119 62
 
0.1%
570976 58
 
< 0.1%
14548 58
 
< 0.1%
530826 58
 
< 0.1%
44254 57
 
< 0.1%
12913 54
 
< 0.1%
Other values (26329) 91499
74.8%
(Missing) 30208
 
24.7%
ValueCountFrequency (%)
1000 1
 
< 0.1%
1002 3
< 0.1%
1005 2
 
< 0.1%
1011 1
 
< 0.1%
1012 5
< 0.1%
ValueCountFrequency (%)
799803 1
< 0.1%
799802 1
< 0.1%
791948 2
< 0.1%
791947 1
< 0.1%
791944 2
< 0.1%

RDNMADR_ONE_NM
Text

MISSING 

Distinct93562
Distinct (%)84.9%
Missing12205
Missing (%)10.0%
Memory size956.2 KiB
2023-07-11T01:16:57.214664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length80
Median length72
Mean length24.70270295
Min length3

Characters and Unicode

Total characters2721645
Distinct characters901
Distinct categories12 ?
Distinct scripts5 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique82094 ?
Unique (%)74.5%

Sample

1st row경기도 안산시 단원구 동산로 63 (원시동)
2nd row경상북도 김천시 자산3길 16 (성내동)
3rd row경기도 안산시 상록구 각골로 127 (본오동)
4th row경기도 성남시 수정구 산성대로 403 (단대동,2층)
5th row경기도 안산시 상록구 중보로 22 (사동, 늘푸른아파트)
ValueCountFrequency (%)
경기도 25387
 
4.4%
서울특별시 16621
 
2.9%
경상북도 8040
 
1.4%
인천광역시 6206
 
1.1%
부산광역시 5876
 
1.0%
경상남도 5713
 
1.0%
전라남도 4504
 
0.8%
충청남도 4377
 
0.8%
충청북도 4191
 
0.7%
전라북도 4119
 
0.7%
Other values (67606) 485503
85.1%
2023-07-11T01:16:57.758756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
465838
 
17.1%
시 104873
 
3.9%
동 103771
 
3.8%
로 88416
 
3.2%
1 83891
 
3.1%
( 78584
 
2.9%
) 78347
 
2.9%
구 69876
 
2.6%
도 67911
 
2.5%
2 60213
 
2.2%
Other values (891) 1519925
55.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1652005
60.7%
Space Separator 465838
 
17.1%
Decimal Number 399030
 
14.7%
Open Punctuation 78594
 
2.9%
Close Punctuation 78358
 
2.9%
Other Punctuation 23022
 
0.8%
Dash Punctuation 22319
 
0.8%
Uppercase Letter 1755
 
0.1%
Math Symbol 500
 
< 0.1%
Lowercase Letter 204
 
< 0.1%
Other values (2) 20
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 104873
 
6.3%
동 103771
 
6.3%
로 88416
 
5.4%
구 69876
 
4.2%
도 67911
 
4.1%
경 46487
 
2.8%
길 43748
 
2.6%
산 35313
 
2.1%
서 34018
 
2.1%
남 33539
 
2.0%
Other values (806) 1024053
62.0%
Uppercase Letter
ValueCountFrequency (%)
B 658
37.5%
A 220
 
12.5%
S 105
 
6.0%
K 84
 
4.8%
C 71
 
4.0%
E 55
 
3.1%
I 54
 
3.1%
L 50
 
2.8%
M 49
 
2.8%
D 49
 
2.8%
Other values (19) 360
20.5%
Lowercase Letter
ValueCountFrequency (%)
e 83
40.7%
a 16
 
7.8%
s 15
 
7.4%
b 13
 
6.4%
r 11
 
5.4%
t 9
 
4.4%
d 7
 
3.4%
w 5
 
2.5%
k 5
 
2.5%
i 5
 
2.5%
Other values (12) 35
17.2%
Other Punctuation
ValueCountFrequency (%)
, 22586
98.1%
. 189
 
0.8%
· 84
 
0.4%
/ 48
 
0.2%
？ 44
 
0.2%
@ 36
 
0.2%
* 16
 
0.1%
& 11
 
< 0.1%
' 5
 
< 0.1%
# 1
 
< 0.1%
Other values (2) 2
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1 83891
21.0%
2 60213
15.1%
3 47262
11.8%
4 37349
9.4%
5 34200
8.6%
0 30188
 
7.6%
6 30186
 
7.6%
7 27440
 
6.9%
8 25257
 
6.3%
9 23044
 
5.8%
Letter Number
ValueCountFrequency (%)
Ⅱ 11
61.1%
Ⅰ 3
 
16.7%
Ⅲ 2
 
11.1%
Ⅳ 2
 
11.1%
Open Punctuation
ValueCountFrequency (%)
( 78584
> 99.9%
[ 10
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 78347
> 99.9%
] 11
 
< 0.1%
Space Separator
ValueCountFrequency (%)
465838
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 22319
100.0%
Math Symbol
ValueCountFrequency (%)
~ 500
100.0%
Other Symbol
ValueCountFrequency (%)
ⓐ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1652003
60.7%
Common 1067663
39.2%
Latin 1976
 
0.1%
Han 2
 
< 0.1%
Greek 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 104873
 
6.3%
동 103771
 
6.3%
로 88416
 
5.4%
구 69876
 
4.2%
도 67911
 
4.1%
경 46487
 
2.8%
길 43748
 
2.6%
산 35313
 
2.1%
서 34018
 
2.1%
남 33539
 
2.0%
Other values (805) 1024051
62.0%
Latin
ValueCountFrequency (%)
B 658
33.3%
A 220
 
11.1%
S 105
 
5.3%
K 84
 
4.3%
e 83
 
4.2%
C 71
 
3.6%
E 55
 
2.8%
I 54
 
2.7%
L 50
 
2.5%
M 49
 
2.5%
Other values (44) 547
27.7%
Common
ValueCountFrequency (%)
465838
43.6%
1 83891
 
7.9%
( 78584
 
7.4%
) 78347
 
7.3%
2 60213
 
5.6%
3 47262
 
4.4%
4 37349
 
3.5%
5 34200
 
3.2%
0 30188
 
2.8%
6 30186
 
2.8%
Other values (20) 121605
 
11.4%
Han
ValueCountFrequency (%)
家 2
100.0%
Greek
ValueCountFrequency (%)
Ι 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1651988
60.7%
ASCII 1069488
39.3%
None 132
 
< 0.1%
Number Forms 18
 
< 0.1%
Compat Jamo 15
 
< 0.1%
CJK 2
 
< 0.1%
Enclosed Alphanum 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
465838
43.6%
1 83891
 
7.8%
( 78584
 
7.3%
) 78347
 
7.3%
2 60213
 
5.6%
3 47262
 
4.4%
4 37349
 
3.5%
5 34200
 
3.2%
0 30188
 
2.8%
6 30186
 
2.8%
Other values (64) 123430
 
11.5%
Hangul
ValueCountFrequency (%)
시 104873
 
6.3%
동 103771
 
6.3%
로 88416
 
5.4%
구 69876
 
4.2%
도 67911
 
4.1%
경 46487
 
2.8%
길 43748
 
2.6%
산 35313
 
2.1%
서 34018
 
2.1%
남 33539
 
2.0%
Other values (803) 1024036
62.0%
None
ValueCountFrequency (%)
· 84
63.6%
？ 44
33.3%
Ι 1
 
0.8%
Ａ 1
 
0.8%
Ｌ 1
 
0.8%
Ｂ 1
 
0.8%
Compat Jamo
ValueCountFrequency (%)
ㆍ 14
93.3%
ㅡ 1
 
6.7%
Number Forms
ValueCountFrequency (%)
Ⅱ 11
61.1%
Ⅰ 3
 
16.7%
Ⅲ 2
 
11.1%
Ⅳ 2
 
11.1%
CJK
ValueCountFrequency (%)
家 2
100.0%
Enclosed Alphanum
ValueCountFrequency (%)
ⓐ 2
100.0%

RDNMADR_TWO_NM
Text

MISSING 

Distinct13755
Distinct (%)56.9%
Missing98199
Missing (%)80.2%
Memory size956.2 KiB
2023-07-11T01:16:58.101780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length68
Median length58
Mean length9.202009759
Min length1

Characters and Unicode

Total characters222523
Distinct characters783
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12355 ?
Unique (%)51.1%

Sample

1st row2층
2nd row세민빌딩 4,5층 (수진동)
3rd row3층
4th row공설운동장 17호
5th row2층
ValueCountFrequency (%)
2층 4460
 
9.4%
3층 3187
 
6.7%
4층 1651
 
3.5%
지하1층 1210
 
2.6%
5층 997
 
2.1%
1층 862
 
1.8%
6층 484
 
1.0%
201호 439
 
0.9%
301호 369
 
0.8%
상가동 365
 
0.8%
Other values (11698) 33374
70.4%
2023-07-11T01:16:58.552053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
23315
 
10.5%
층 15483
 
7.0%
동 13564
 
6.1%
( 11708
 
5.3%
) 11697
 
5.3%
2 11047
 
5.0%
0 9875
 
4.4%
1 9796
 
4.4%
호 7962
 
3.6%
3 7702
 
3.5%
Other values (773) 100374
45.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 114492
51.5%
Decimal Number 52182
23.5%
Space Separator 23315
 
10.5%
Open Punctuation 11708
 
5.3%
Close Punctuation 11697
 
5.3%
Other Punctuation 4877
 
2.2%
Uppercase Letter 2235
 
1.0%
Math Symbol 1139
 
0.5%
Dash Punctuation 633
 
0.3%
Lowercase Letter 223
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
층 15483
 
13.5%
동 13564
 
11.8%
호 7962
 
7.0%
지 3194
 
2.8%
하 2288
 
2.0%
빌 1937
 
1.7%
딩 1785
 
1.6%
가 1780
 
1.6%
라 1654
 
1.4%
자 1568
 
1.4%
Other values (692) 63277
55.3%
Uppercase Letter
ValueCountFrequency (%)
B 882
39.5%
A 214
 
9.6%
S 114
 
5.1%
C 99
 
4.4%
K 95
 
4.3%
E 75
 
3.4%
T 75
 
3.4%
M 68
 
3.0%
I 66
 
3.0%
D 65
 
2.9%
Other values (15) 482
21.6%
Lowercase Letter
ValueCountFrequency (%)
e 52
23.3%
b 27
12.1%
a 20
 
9.0%
r 16
 
7.2%
l 15
 
6.7%
c 15
 
6.7%
s 10
 
4.5%
o 10
 
4.5%
d 10
 
4.5%
w 8
 
3.6%
Other values (15) 40
17.9%
Other Punctuation
ValueCountFrequency (%)
, 4721
96.8%
. 100
 
2.1%
& 15
 
0.3%
/ 12
 
0.2%
@ 9
 
0.2%
· 9
 
0.2%
' 4
 
0.1%
: 3
 
0.1%
？ 2
 
< 0.1%
＆ 1
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
2 11047
21.2%
0 9875
18.9%
1 9796
18.8%
3 7702
14.8%
4 4774
9.1%
5 3394
 
6.5%
6 2139
 
4.1%
7 1594
 
3.1%
8 1047
 
2.0%
9 814
 
1.6%
Letter Number
ValueCountFrequency (%)
Ⅱ 13
59.1%
Ⅰ 6
27.3%
Ⅲ 2
 
9.1%
Ⅳ 1
 
4.5%
Math Symbol
ValueCountFrequency (%)
~ 1134
99.6%
∼ 5
 
0.4%
Space Separator
ValueCountFrequency (%)
23315
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11708
100.0%
Close Punctuation
ValueCountFrequency (%)
) 11697
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 633
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 114487
51.4%
Common 105551
47.4%
Latin 2480
 
1.1%
Han 5
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
층 15483
 
13.5%
동 13564
 
11.8%
호 7962
 
7.0%
지 3194
 
2.8%
하 2288
 
2.0%
빌 1937
 
1.7%
딩 1785
 
1.6%
가 1780
 
1.6%
라 1654
 
1.4%
자 1568
 
1.4%
Other values (688) 63272
55.3%
Latin
ValueCountFrequency (%)
B 882
35.6%
A 214
 
8.6%
S 114
 
4.6%
C 99
 
4.0%
K 95
 
3.8%
E 75
 
3.0%
T 75
 
3.0%
M 68
 
2.7%
I 66
 
2.7%
D 65
 
2.6%
Other values (44) 727
29.3%
Common
ValueCountFrequency (%)
23315
22.1%
( 11708
11.1%
) 11697
11.1%
2 11047
10.5%
0 9875
9.4%
1 9796
9.3%
3 7702
 
7.3%
4 4774
 
4.5%
, 4721
 
4.5%
5 3394
 
3.2%
Other values (17) 7522
 
7.1%
Han
ValueCountFrequency (%)
家 2
40.0%
內 1
20.0%
巖 1
20.0%
如 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 114484
51.4%
ASCII 107992
48.5%
Number Forms 22
 
< 0.1%
None 12
 
< 0.1%
Math Operators 5
 
< 0.1%
CJK 5
 
< 0.1%
Compat Jamo 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
23315
21.6%
( 11708
10.8%
) 11697
10.8%
2 11047
10.2%
0 9875
9.1%
1 9796
9.1%
3 7702
 
7.1%
4 4774
 
4.4%
, 4721
 
4.4%
5 3394
 
3.1%
Other values (63) 9963
9.2%
Hangul
ValueCountFrequency (%)
층 15483
 
13.5%
동 13564
 
11.8%
호 7962
 
7.0%
지 3194
 
2.8%
하 2288
 
2.0%
빌 1937
 
1.7%
딩 1785
 
1.6%
가 1780
 
1.6%
라 1654
 
1.4%
자 1568
 
1.4%
Other values (686) 63269
55.3%
Number Forms
ValueCountFrequency (%)
Ⅱ 13
59.1%
Ⅰ 6
27.3%
Ⅲ 2
 
9.1%
Ⅳ 1
 
4.5%
None
ValueCountFrequency (%)
· 9
75.0%
？ 2
 
16.7%
＆ 1
 
8.3%
Math Operators
ValueCountFrequency (%)
∼ 5
100.0%
CJK
ValueCountFrequency (%)
家 2
40.0%
內 1
20.0%
巖 1
20.0%
如 1
20.0%
Compat Jamo
ValueCountFrequency (%)
ㄴ 2
66.7%
ㅈ 1
33.3%

ZIP_NO_VALUE
Real number (ℝ)

MISSING 

Distinct26996
Distinct (%)28.6%
Missing28078
Missing (%)22.9%
Infinite0
Infinite (%)0.0%
Mean135308.899
Minimum1000
Maximum799821
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size956.2 KiB
2023-07-11T01:16:58.654700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile5282
Q115396
median36957
Q361966
95-th percentile681821
Maximum799821
Range798821
Interquartile range (IQR)46570

Descriptive statistics

Standard deviation215334.8882
Coefficient of variation (CV)1.591431825
Kurtosis1.540715449
Mean135308.899
Median Absolute Deviation (MAD)22211
Skewness1.752265965
Sum1.276003511 × 1010
Variance4.636911407 × 1010
MonotonicityNot monotonic
2023-07-11T01:16:58.740206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46726 97
 
0.1%
10071 81
 
0.1%
31156 76
 
0.1%
15010 68
 
0.1%
30119 62
 
0.1%
570976 58
 
< 0.1%
530826 58
 
< 0.1%
14548 58
 
< 0.1%
44254 57
 
< 0.1%
305301 56
 
< 0.1%
Other values (26986) 93632
76.5%
(Missing) 28078
 
22.9%
ValueCountFrequency (%)
1000 1
 
< 0.1%
1002 3
< 0.1%
1005 2
 
< 0.1%
1011 1
 
< 0.1%
1012 5
< 0.1%
ValueCountFrequency (%)
799821 1
< 0.1%
799811 1
< 0.1%
799803 1
< 0.1%
799802 1
< 0.1%
791948 2
< 0.1%

FCLTY_ADDR_ONE_NM
Text

MISSING 

Distinct84749
Distinct (%)82.4%
Missing19552
Missing (%)16.0%
Memory size956.2 KiB
2023-07-11T01:16:59.143251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length76
Median length65
Mean length21.74499412
Min length2

Characters and Unicode

Total characters2236016
Distinct characters978
Distinct categories12 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique73661 ?
Unique (%)71.6%

Sample

1st row경상북도 예지리 680
2nd row경상북도 김천시 성내동 161-17
3rd row경기도 안산시 상록구 본오동 1118-10
4th row경기 성남시 수정구 산성대로 403
5th row경기도 안산시 상록구 사동 1533 늘푸른아파트
ValueCountFrequency (%)
경기도 19574
 
3.9%
서울특별시 12817
 
2.6%
경상북도 7337
 
1.5%
경상남도 5167
 
1.0%
인천광역시 4948
 
1.0%
전라남도 4586
 
0.9%
경기 4214
 
0.8%
충청남도 4158
 
0.8%
충청북도 3892
 
0.8%
부산광역시 3756
 
0.8%
Other values (64873) 425617
85.8%
2023-07-11T01:16:59.712162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
404387
 
18.1%
시 91265
 
4.1%
1 79181
 
3.5%
동 77731
 
3.5%
구 63915
 
2.9%
도 58713
 
2.6%
- 54672
 
2.4%
2 51193
 
2.3%
경 46232
 
2.1%
3 42390
 
1.9%
Other values (968) 1266337
56.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1366939
61.1%
Space Separator 404387
 
18.1%
Decimal Number 381751
 
17.1%
Dash Punctuation 54672
 
2.4%
Open Punctuation 9155
 
0.4%
Close Punctuation 9013
 
0.4%
Other Punctuation 6074
 
0.3%
Uppercase Letter 3172
 
0.1%
Lowercase Letter 656
 
< 0.1%
Math Symbol 127
 
< 0.1%
Other values (2) 70
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 91265
 
6.7%
동 77731
 
5.7%
구 63915
 
4.7%
도 58713
 
4.3%
경 46232
 
3.4%
남 33826
 
2.5%
산 30662
 
2.2%
서 29787
 
2.2%
북 27448
 
2.0%
로 26663
 
2.0%
Other values (881) 880697
64.4%
Uppercase Letter
ValueCountFrequency (%)
B 403
 
12.7%
S 268
 
8.4%
C 236
 
7.4%
A 221
 
7.0%
K 208
 
6.6%
I 172
 
5.4%
E 152
 
4.8%
L 151
 
4.8%
G 150
 
4.7%
M 142
 
4.5%
Other values (16) 1069
33.7%
Lowercase Letter
ValueCountFrequency (%)
e 147
22.4%
a 62
9.5%
l 52
 
7.9%
o 46
 
7.0%
r 41
 
6.2%
i 38
 
5.8%
s 36
 
5.5%
t 35
 
5.3%
n 33
 
5.0%
c 29
 
4.4%
Other values (15) 137
20.9%
Other Punctuation
ValueCountFrequency (%)
, 5690
93.7%
. 176
 
2.9%
· 61
 
1.0%
& 46
 
0.8%
/ 43
 
0.7%
@ 24
 
0.4%
？ 18
 
0.3%
' 7
 
0.1%
: 3
 
< 0.1%
" 2
 
< 0.1%
Other values (3) 4
 
0.1%
Decimal Number
ValueCountFrequency (%)
1 79181
20.7%
2 51193
13.4%
3 42390
11.1%
4 35990
9.4%
5 34214
9.0%
6 31373
 
8.2%
7 28736
 
7.5%
0 27162
 
7.1%
8 26733
 
7.0%
9 24779
 
6.5%
Letter Number
ValueCountFrequency (%)
Ⅱ 33
49.3%
Ⅰ 23
34.3%
Ⅲ 7
 
10.4%
Ⅳ 3
 
4.5%
Ⅵ 1
 
1.5%
Math Symbol
ValueCountFrequency (%)
~ 123
96.9%
+ 3
 
2.4%
→ 1
 
0.8%
Space Separator
ValueCountFrequency (%)
404387
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 54672
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9155
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9013
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1366925
61.1%
Common 865179
38.7%
Latin 3895
 
0.2%
Han 17
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 91265
 
6.7%
동 77731
 
5.7%
구 63915
 
4.7%
도 58713
 
4.3%
경 46232
 
3.4%
남 33826
 
2.5%
산 30662
 
2.2%
서 29787
 
2.2%
북 27448
 
2.0%
로 26663
 
2.0%
Other values (868) 880683
64.4%
Latin
ValueCountFrequency (%)
B 403
 
10.3%
S 268
 
6.9%
C 236
 
6.1%
A 221
 
5.7%
K 208
 
5.3%
I 172
 
4.4%
E 152
 
3.9%
L 151
 
3.9%
G 150
 
3.9%
e 147
 
3.8%
Other values (46) 1787
45.9%
Common
ValueCountFrequency (%)
404387
46.7%
1 79181
 
9.2%
- 54672
 
6.3%
2 51193
 
5.9%
3 42390
 
4.9%
4 35990
 
4.2%
5 34214
 
4.0%
6 31373
 
3.6%
7 28736
 
3.3%
0 27162
 
3.1%
Other values (20) 75881
 
8.8%
Han
ValueCountFrequency (%)
家 3
17.6%
山 2
11.8%
美 1
 
5.9%
知 1
 
5.9%
坪 1
 
5.9%
岐 1
 
5.9%
岩 1
 
5.9%
華 1
 
5.9%
平 1
 
5.9%
花 1
 
5.9%
Other values (4) 4
23.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1366922
61.1%
ASCII 868926
38.9%
None 83
 
< 0.1%
Number Forms 67
 
< 0.1%
CJK 17
 
< 0.1%
Arrows 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
404387
46.5%
1 79181
 
9.1%
- 54672
 
6.3%
2 51193
 
5.9%
3 42390
 
4.9%
4 35990
 
4.1%
5 34214
 
3.9%
6 31373
 
3.6%
7 28736
 
3.3%
0 27162
 
3.1%
Other values (67) 79628
 
9.2%
Hangul
ValueCountFrequency (%)
시 91265
 
6.7%
동 77731
 
5.7%
구 63915
 
4.7%
도 58713
 
4.3%
경 46232
 
3.4%
남 33826
 
2.5%
산 30662
 
2.2%
서 29787
 
2.2%
북 27448
 
2.0%
로 26663
 
2.0%
Other values (867) 880680
64.4%
None
ValueCountFrequency (%)
· 61
73.5%
？ 18
 
21.7%
㈜ 3
 
3.6%
＆ 1
 
1.2%
Number Forms
ValueCountFrequency (%)
Ⅱ 33
49.3%
Ⅰ 23
34.3%
Ⅲ 7
 
10.4%
Ⅳ 3
 
4.5%
Ⅵ 1
 
1.5%
CJK
ValueCountFrequency (%)
家 3
17.6%
山 2
11.8%
美 1
 
5.9%
知 1
 
5.9%
坪 1
 
5.9%
岐 1
 
5.9%
岩 1
 
5.9%
華 1
 
5.9%
平 1
 
5.9%
花 1
 
5.9%
Other values (4) 4
23.5%
Arrows
ValueCountFrequency (%)
→ 1
100.0%

FCLTY_ADDR_TWO_NM
Text

MISSING 

Distinct15277
Distinct (%)50.9%
Missing92372
Missing (%)75.5%
Memory size956.2 KiB
2023-07-11T01:17:00.175547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length68
Median length59
Mean length7.399213569
Min length1

Characters and Unicode

Total characters222043
Distinct characters835
Distinct categories13 ?
Distinct scripts4 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13982 ?
Unique (%)46.6%

Sample

1st row207호
2nd row자산경로당
3rd row, 501호
4th row모고마을 부근
5th row범학마을 부근
ValueCountFrequency (%)
2층 4366
 
8.7%
3층 3493
 
7.0%
4층 1709
 
3.4%
지하1층 1473
 
2.9%
5층 1010
 
2.0%
1층 713
 
1.4%
6층 446
 
0.9%
지층 428
 
0.9%
201호 421
 
0.8%
상가동 419
 
0.8%
Other values (15598) 35537
71.1%
2023-07-11T01:17:00.663030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20104
 
9.1%
층 15780
 
7.1%
2 12029
 
5.4%
1 11415
 
5.1%
0 10707
 
4.8%
3 8917
 
4.0%
호 8019
 
3.6%
동 7319
 
3.3%
4 5703
 
2.6%
( 4912
 
2.2%
Other values (825) 117138
52.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 121002
54.5%
Decimal Number 60971
27.5%
Space Separator 20104
 
9.1%
Open Punctuation 4913
 
2.2%
Close Punctuation 4906
 
2.2%
Other Punctuation 4383
 
2.0%
Uppercase Letter 2207
 
1.0%
Dash Punctuation 2175
 
1.0%
Math Symbol 1158
 
0.5%
Lowercase Letter 198
 
0.1%
Other values (3) 26
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
층 15780
 
13.0%
호 8019
 
6.6%
동 7319
 
6.0%
지 4078
 
3.4%
하 2654
 
2.2%
빌 2157
 
1.8%
리 2086
 
1.7%
딩 1986
 
1.6%
라 1935
 
1.6%
자 1811
 
1.5%
Other values (736) 73177
60.5%
Uppercase Letter
ValueCountFrequency (%)
B 875
39.6%
A 191
 
8.7%
S 115
 
5.2%
C 112
 
5.1%
K 97
 
4.4%
T 73
 
3.3%
M 69
 
3.1%
E 69
 
3.1%
D 67
 
3.0%
I 62
 
2.8%
Other values (15) 477
21.6%
Lowercase Letter
ValueCountFrequency (%)
e 55
27.8%
b 20
 
10.1%
l 13
 
6.6%
r 13
 
6.6%
a 12
 
6.1%
o 9
 
4.5%
i 8
 
4.0%
s 8
 
4.0%
c 8
 
4.0%
d 7
 
3.5%
Other values (15) 45
22.7%
Other Punctuation
ValueCountFrequency (%)
, 4072
92.9%
. 161
 
3.7%
: 70
 
1.6%
/ 21
 
0.5%
& 17
 
0.4%
@ 13
 
0.3%
＠ 11
 
0.3%
· 8
 
0.2%
' 4
 
0.1%
; 2
 
< 0.1%
Other values (4) 4
 
0.1%
Decimal Number
ValueCountFrequency (%)
2 12029
19.7%
1 11415
18.7%
0 10707
17.6%
3 8917
14.6%
4 5703
9.4%
5 4239
 
7.0%
6 2803
 
4.6%
7 2185
 
3.6%
8 1628
 
2.7%
9 1345
 
2.2%
Letter Number
ValueCountFrequency (%)
Ⅱ 13
56.5%
Ⅰ 6
26.1%
Ⅲ 3
 
13.0%
Ⅳ 1
 
4.3%
Math Symbol
ValueCountFrequency (%)
~ 1151
99.4%
∼ 5
 
0.4%
+ 2
 
0.2%
Open Punctuation
ValueCountFrequency (%)
( 4912
> 99.9%
[ 1
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 4905
> 99.9%
] 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
20104
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2175
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 120993
54.5%
Common 98613
44.4%
Latin 2428
 
1.1%
Han 9
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
층 15780
 
13.0%
호 8019
 
6.6%
동 7319
 
6.0%
지 4078
 
3.4%
하 2654
 
2.2%
빌 2157
 
1.8%
리 2086
 
1.7%
딩 1986
 
1.6%
라 1935
 
1.6%
자 1811
 
1.5%
Other values (732) 73168
60.5%
Latin
ValueCountFrequency (%)
B 875
36.0%
A 191
 
7.9%
S 115
 
4.7%
C 112
 
4.6%
K 97
 
4.0%
T 73
 
3.0%
M 69
 
2.8%
E 69
 
2.8%
D 67
 
2.8%
I 62
 
2.6%
Other values (44) 698
28.7%
Common
ValueCountFrequency (%)
20104
20.4%
2 12029
12.2%
1 11415
11.6%
0 10707
10.9%
3 8917
9.0%
4 5703
 
5.8%
( 4912
 
5.0%
) 4905
 
5.0%
5 4239
 
4.3%
, 4072
 
4.1%
Other values (25) 11610
11.8%
Han
ValueCountFrequency (%)
內 5
55.6%
家 2
 
22.2%
巖 1
 
11.1%
如 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 120985
54.5%
ASCII 100992
45.5%
Number Forms 23
 
< 0.1%
None 21
 
< 0.1%
CJK 9
 
< 0.1%
Compat Jamo 8
 
< 0.1%
Math Operators 5
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
20104
19.9%
2 12029
11.9%
1 11415
11.3%
0 10707
10.6%
3 8917
8.8%
4 5703
 
5.6%
( 4912
 
4.9%
) 4905
 
4.9%
5 4239
 
4.2%
, 4072
 
4.0%
Other values (70) 13989
13.9%
Hangul
ValueCountFrequency (%)
층 15780
 
13.0%
호 8019
 
6.6%
동 7319
 
6.0%
지 4078
 
3.4%
하 2654
 
2.2%
빌 2157
 
1.8%
리 2086
 
1.7%
딩 1986
 
1.6%
라 1935
 
1.6%
자 1811
 
1.5%
Other values (726) 73160
60.5%
Number Forms
ValueCountFrequency (%)
Ⅱ 13
56.5%
Ⅰ 6
26.1%
Ⅲ 3
 
13.0%
Ⅳ 1
 
4.3%
None
ValueCountFrequency (%)
＠ 11
52.4%
· 8
38.1%
＆ 1
 
4.8%
？ 1
 
4.8%
Math Operators
ValueCountFrequency (%)
∼ 5
100.0%
CJK
ValueCountFrequency (%)
內 5
55.6%
家 2
 
22.2%
巖 1
 
11.1%
如 1
 
11.1%
Compat Jamo
ValueCountFrequency (%)
ㄴ 2
25.0%
ㅇ 2
25.0%
ㅕ 1
12.5%
ㅍ 1
12.5%
ㅎ 1
12.5%
ㅜ 1
12.5%

FCLTY_LO
Real number (ℝ)

MISSING  ZEROS 

Distinct97781
Distinct (%)84.4%
Missing6513
Missing (%)5.3%
Infinite0
Infinite (%)0.0%
Mean124.9545154
Minimum0
Maximum130.9102031
Zeros2363
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size956.2 KiB
2023-07-11T01:17:00.757730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile126.5251245
Q1126.8993584
median127.1277323
Q3128.3383574
95-th percentile129.1711546
Maximum130.9102031
Range130.9102031
Interquartile range (IQR)1.438998956

Descriptive statistics

Standard deviation18.06129468
Coefficient of variation (CV)0.1445429532
Kurtosis43.75299949
Mean124.9545154
Median Absolute Deviation (MAD)0.3670302286
Skewness-6.755245788
Sum14478229.79
Variance326.2103655
MonotonicityNot monotonic
2023-07-11T01:17:00.848896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2363
 
1.9%
127.265434 335
 
0.3%
127.4548071 88
 
0.1%
127.2797136 37
 
< 0.1%
127.5095702 36
 
< 0.1%
127.2551387 28
 
< 0.1%
127.2653447 23
 
< 0.1%
126.7314877 20
 
< 0.1%
126.7368957 17
 
< 0.1%
126.9975552 17
 
< 0.1%
Other values (97771) 112904
92.3%
(Missing) 6513
 
5.3%
ValueCountFrequency (%)
0 2363
1.9%
37.40161224 1
 
< 0.1%
37.45802058 1
 
< 0.1%
37.5422951 1
 
< 0.1%
37.54254671 1
 
< 0.1%
ValueCountFrequency (%)
130.9102031 1
< 0.1%
130.909833 1
< 0.1%
130.9095877 1
< 0.1%
130.9091235 1
< 0.1%
130.9090284 1
< 0.1%

FCLTY_LA
Real number (ℝ)

MISSING  ZEROS 

Distinct95025
Distinct (%)82.0%
Missing6513
Missing (%)5.3%
Infinite0
Infinite (%)0.0%
Mean35.8258388
Minimum0
Maximum127.1315892
Zeros2363
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size956.2 KiB
2023-07-11T01:17:00.939476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile34.76135252
Q135.57036508
median36.82473896
Q337.4939448
95-th percentile37.71379065
Maximum127.1315892
Range127.1315892
Interquartile range (IQR)1.923579721

Descriptive statistics

Standard deviation5.313004646
Coefficient of variation (CV)0.1483009142
Kurtosis43.65835125
Mean35.8258388
Median Absolute Deviation (MAD)0.7470502042
Skewness-5.977612809
Sum4151068.29
Variance28.22801837
MonotonicityNot monotonic
2023-07-11T01:17:01.036104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2363
 
1.9%
36.49787653 335
 
0.3%
36.31176963 88
 
0.1%
37.00799499 37
 
< 0.1%
37.83144131 36
 
< 0.1%
37.42939419 28
 
< 0.1%
36.49785871 23
 
< 0.1%
37.44734879 20
 
< 0.1%
35.72960785 17
 
< 0.1%
37.56380777 17
 
< 0.1%
Other values (95015) 112904
92.3%
(Missing) 6513
 
5.3%
ValueCountFrequency (%)
0 2363
1.9%
33.21167523 1
 
< 0.1%
33.21598389 1
 
< 0.1%
33.21828009 1
 
< 0.1%
33.22083937 1
 
< 0.1%
ValueCountFrequency (%)
127.1315892 1
< 0.1%
127.1315029 1
< 0.1%
127.1308959 1
< 0.1%
127.054859 1
< 0.1%
126.7342261 1
< 0.1%

FCLTY_TEL_NO
Text

MISSING 

Distinct44169
Distinct (%)66.6%
Missing56103
Missing (%)45.8%
Memory size956.2 KiB
2023-07-11T01:17:01.284958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.81989499
Min length7

Characters and Unicode

Total characters783399
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40018 ?
Unique (%)60.4%

Sample

1st row031-492-3392
2nd row054-421-2318
3rd row055-960-5481
4th row055-960-5481
5th row031-407-6785
ValueCountFrequency (%)
054-639-3813 456
 
0.7%
055-359-5780 372
 
0.6%
061-749-6662 215
 
0.3%
032-340-0896 213
 
0.3%
055-650-4723 198
 
0.3%
053-810-6158 174
 
0.3%
031-000-0000 165
 
0.2%
031-8082-5647 148
 
0.2%
062-510-1267 134
 
0.2%
063-539-5443 129
 
0.2%
Other values (44159) 64074
96.7%
2023-07-11T01:17:01.631350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 130666
16.7%
0 121704
15.5%
3 81174
10.4%
5 74504
9.5%
2 71793
9.2%
4 60078
7.7%
1 58811
7.5%
6 50491
 
6.4%
7 49222
 
6.3%
8 45201
 
5.8%
Other values (2) 39755
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 652685
83.3%
Dash Punctuation 130666
 
16.7%
Close Punctuation 48
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 121704
18.6%
3 81174
12.4%
5 74504
11.4%
2 71793
11.0%
4 60078
9.2%
1 58811
9.0%
6 50491
7.7%
7 49222
7.5%
8 45201
 
6.9%
9 39707
 
6.1%
Dash Punctuation
ValueCountFrequency (%)
- 130666
100.0%
Close Punctuation
ValueCountFrequency (%)
) 48
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 783399
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 130666
16.7%
0 121704
15.5%
3 81174
10.4%
5 74504
9.5%
2 71793
9.2%
4 60078
7.7%
1 58811
7.5%
6 50491
 
6.4%
7 49222
 
6.3%
8 45201
 
5.8%
Other values (2) 39755
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 783399
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 130666
16.7%
0 121704
15.5%
3 81174
10.4%
5 74504
9.5%
2 71793
9.2%
4 60078
7.7%
1 58811
7.5%
6 50491
 
6.4%
7 49222
 
6.3%
8 45201
 
5.8%
Other values (2) 39755
 
5.1%

FCLTY_HMPG_URL
Text

MISSING 

Distinct2573
Distinct (%)65.0%
Missing118425
Missing (%)96.8%
Memory size956.2 KiB
2023-07-11T01:17:01.807970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length164
Median length78
Mean length23.6314459
Min length1

Characters and Unicode

Total characters93486
Distinct characters316
Distinct categories13 ?
Distinct scripts3 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2166 ?
Unique (%)54.8%

Sample

1st rowWww.spkumdo.com
2nd rowhttps://wolpiteam.modoo.at/
3rd rowWww.noblesporex.com
4th rowwww.rushsports.kr
5th rowrealswimmingschool.modoo.at
ValueCountFrequency (%)
없음 141
 
3.5%
https://yeyak.hsuco.or.kr 69
 
1.7%
없슴 50
 
1.2%
https://sports.seogwipo.go.kr 42
 
1.0%
폐업 29
 
0.7%
http:///www.geojesp.co.kr/_main/main.html 28
 
0.7%
시설관리공단 21
 
0.5%
서울시 18
 
0.4%
x 18
 
0.4%
http://www.dssiseol.or.kr 17
 
0.4%
Other values (2571) 3577
89.2%
2023-07-11T01:17:02.102072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 8541
 
9.1%
o 6801
 
7.3%
/ 6282
 
6.7%
t 5921
 
6.3%
w 5914
 
6.3%
r 4448
 
4.8%
s 4267
 
4.6%
n 3559
 
3.8%
e 3422
 
3.7%
a 3364
 
3.6%
Other values (306) 40967
43.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 67433
72.1%
Other Punctuation 17522
 
18.7%
Decimal Number 3882
 
4.2%
Other Letter 2420
 
2.6%
Uppercase Letter 1340
 
1.4%
Connector Punctuation 315
 
0.3%
Math Symbol 266
 
0.3%
Dash Punctuation 143
 
0.2%
Close Punctuation 55
 
0.1%
Space Separator 54
 
0.1%
Other values (3) 56
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 209
 
8.6%
없 191
 
7.9%
음 142
 
5.9%
군 90
 
3.7%
구 75
 
3.1%
업 53
 
2.2%
관 50
 
2.1%
슴 50
 
2.1%
리 48
 
2.0%
스 45
 
1.9%
Other values (221) 1467
60.6%
Lowercase Letter
ValueCountFrequency (%)
o 6801
 
10.1%
t 5921
 
8.8%
w 5914
 
8.8%
r 4448
 
6.6%
s 4267
 
6.3%
n 3559
 
5.3%
e 3422
 
5.1%
a 3364
 
5.0%
c 3230
 
4.8%
p 3222
 
4.8%
Other values (16) 23285
34.5%
Uppercase Letter
ValueCountFrequency (%)
E 181
13.5%
C 179
13.4%
B 161
12.0%
W 110
 
8.2%
A 102
 
7.6%
D 75
 
5.6%
I 55
 
4.1%
P 53
 
4.0%
F 46
 
3.4%
M 42
 
3.1%
Other values (16) 336
25.1%
Other Punctuation
ValueCountFrequency (%)
. 8541
48.7%
/ 6282
35.9%
: 1893
 
10.8%
% 499
 
2.8%
? 191
 
1.1%
& 46
 
0.3%
@ 43
 
0.2%
# 13
 
0.1%
! 9
 
0.1%
, 3
 
< 0.1%
Other values (2) 2
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
0 926
23.9%
1 615
15.8%
2 425
10.9%
4 344
 
8.9%
9 323
 
8.3%
3 316
 
8.1%
8 295
 
7.6%
5 253
 
6.5%
7 197
 
5.1%
6 188
 
4.8%
Math Symbol
ValueCountFrequency (%)
= 263
98.9%
> 3
 
1.1%
Close Punctuation
ValueCountFrequency (%)
) 49
89.1%
] 6
 
10.9%
Open Punctuation
ValueCountFrequency (%)
( 48
88.9%
[ 6
 
11.1%
Connector Punctuation
ValueCountFrequency (%)
_ 315
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 143
100.0%
Space Separator
ValueCountFrequency (%)
54
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 1
100.0%
Initial Punctuation
ValueCountFrequency (%)
‘ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 68773
73.6%
Common 22293
 
23.8%
Hangul 2420
 
2.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 209
 
8.6%
없 191
 
7.9%
음 142
 
5.9%
군 90
 
3.7%
구 75
 
3.1%
업 53
 
2.2%
관 50
 
2.1%
슴 50
 
2.1%
리 48
 
2.0%
스 45
 
1.9%
Other values (221) 1467
60.6%
Latin
ValueCountFrequency (%)
o 6801
 
9.9%
t 5921
 
8.6%
w 5914
 
8.6%
r 4448
 
6.5%
s 4267
 
6.2%
n 3559
 
5.2%
e 3422
 
5.0%
a 3364
 
4.9%
c 3230
 
4.7%
p 3222
 
4.7%
Other values (42) 24625
35.8%
Common
ValueCountFrequency (%)
. 8541
38.3%
/ 6282
28.2%
: 1893
 
8.5%
0 926
 
4.2%
1 615
 
2.8%
% 499
 
2.2%
2 425
 
1.9%
4 344
 
1.5%
9 323
 
1.4%
3 316
 
1.4%
Other values (23) 2129
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 91063
97.4%
Hangul 2419
 
2.6%
Punctuation 2
 
< 0.1%
None 1
 
< 0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 8541
 
9.4%
o 6801
 
7.5%
/ 6282
 
6.9%
t 5921
 
6.5%
w 5914
 
6.5%
r 4448
 
4.9%
s 4267
 
4.7%
n 3559
 
3.9%
e 3422
 
3.8%
a 3364
 
3.7%
Other values (72) 38544
42.3%
Hangul
ValueCountFrequency (%)
시 209
 
8.6%
없 191
 
7.9%
음 142
 
5.9%
군 90
 
3.7%
구 75
 
3.1%
업 53
 
2.2%
관 50
 
2.1%
슴 50
 
2.1%
리 48
 
2.0%
스 45
 
1.9%
Other values (220) 1466
60.6%
Punctuation
ValueCountFrequency (%)
’ 1
50.0%
‘ 1
50.0%
None
ValueCountFrequency (%)
？ 1
100.0%
Compat Jamo
ValueCountFrequency (%)
ㅅ 1
100.0%

CTPRVN_CD
Real number (ℝ)

Distinct17
Distinct (%)< 0.1%
Missing32
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3551495313
Minimum1100000000
Maximum5000000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size956.2 KiB
2023-07-11T01:17:02.185789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1100000000
5-th percentile1100000000
Q12800000000
median4100000000
Q34500000000
95-th percentile4800000000
Maximum5000000000
Range3900000000
Interquartile range (IQR)1700000000

Descriptive statistics

Standard deviation1237336544
Coefficient of variation (CV)0.3483987547
Kurtosis-0.3959741281
Mean3551495313
Median Absolute Deviation (MAD)600000000
Skewness-0.9614808516
Sum4.345219 × 1014
Variance1.531001724 × 1018
MonotonicityNot monotonic
2023-07-11T01:17:02.254794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
4100000000 28800
23.5%
1100000000 18503
15.1%
4700000000 11220
 
9.2%
4800000000 8579
 
7.0%
4600000000 6613
 
5.4%
2600000000 6294
 
5.1%
2800000000 6137
 
5.0%
4300000000 5381
 
4.4%
4200000000 5374
 
4.4%
4400000000 4908
 
4.0%
Other values (7) 20540
16.8%
ValueCountFrequency (%)
1100000000 18503
15.1%
2600000000 6294
 
5.1%
2700000000 4482
 
3.7%
2800000000 6137
 
5.0%
2900000000 3315
 
2.7%
ValueCountFrequency (%)
5000000000 1897
 
1.6%
4800000000 8579
7.0%
4700000000 11220
9.2%
4600000000 6613
5.4%
4500000000 4328
 
3.5%

CTPRVN_NM
Text

Distinct17
Distinct (%)< 0.1%
Missing32
Missing (%)< 0.1%
Memory size956.2 KiB
2023-07-11T01:17:02.381484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length7
Median length5
Mean length4.147806684
Min length3

Characters and Unicode

Total characters507480
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경상북도
2nd row경기도
3rd row경상북도
4th row경기도
5th row경기도
ValueCountFrequency (%)
경기도 28800
23.5%
서울특별시 18503
15.1%
경상북도 11220
 
9.2%
경상남도 8579
 
7.0%
전라남도 6613
 
5.4%
부산광역시 6294
 
5.1%
인천광역시 6137
 
5.0%
충청북도 5381
 
4.4%
강원도 5374
 
4.4%
충청남도 4908
 
4.0%
Other values (7) 20540
16.8%
2023-07-11T01:17:02.591984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
도 77100
15.2%
경 48599
 
9.6%
시 45249
 
8.9%
광 29402
 
5.8%
기 28800
 
5.7%
역 26087
 
5.1%
울 21283
 
4.2%
별 21059
 
4.1%
특 21059
 
4.1%
북 20929
 
4.1%
Other values (21) 167913
33.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 507480
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 77100
15.2%
경 48599
 
9.6%
시 45249
 
8.9%
광 29402
 
5.8%
기 28800
 
5.7%
역 26087
 
5.1%
울 21283
 
4.2%
별 21059
 
4.1%
특 21059
 
4.1%
북 20929
 
4.1%
Other values (21) 167913
33.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 507480
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 77100
15.2%
경 48599
 
9.6%
시 45249
 
8.9%
광 29402
 
5.8%
기 28800
 
5.7%
역 26087
 
5.1%
울 21283
 
4.2%
별 21059
 
4.1%
특 21059
 
4.1%
북 20929
 
4.1%
Other values (21) 167913
33.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 507480
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
도 77100
15.2%
경 48599
 
9.6%
시 45249
 
8.9%
광 29402
 
5.8%
기 28800
 
5.7%
역 26087
 
5.1%
울 21283
 
4.2%
별 21059
 
4.1%
특 21059
 
4.1%
북 20929
 
4.1%
Other values (21) 167913
33.1%

SIGNGU_CD
Real number (ℝ)

Distinct246
Distinct (%)0.2%
Missing32
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3585646412
Minimum1100000000
Maximum5013000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size956.2 KiB
2023-07-11T01:17:02.683777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1100000000
5-th percentile1138000000
Q12817000000
median4137000000
Q34513000000
95-th percentile4827000000
Maximum5013000000
Range3913000000
Interquartile range (IQR)1696000000

Descriptive statistics

Standard deviation1234278837
Coefficient of variation (CV)0.3442277055
Kurtosis-0.4199280716
Mean3585646412
Median Absolute Deviation (MAD)582000000
Skewness-0.9506019066
Sum4.387002529 × 1014
Variance1.523444248 × 1018
MonotonicityNot monotonic
2023-07-11T01:17:02.776390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4111000000 2681
 
2.2%
4311000000 2570
 
2.1%
4128000000 2263
 
1.8%
4159000000 2010
 
1.6%
1168000000 1997
 
1.6%
4146000000 1926
 
1.6%
4812000000 1875
 
1.5%
4113000000 1846
 
1.5%
4119000000 1795
 
1.5%
4511000000 1708
 
1.4%
Other values (236) 101678
83.1%
ValueCountFrequency (%)
1100000000 96
 
0.1%
1111000000 480
0.4%
1114000000 556
0.5%
1117000000 348
0.3%
1120000000 509
0.4%
ValueCountFrequency (%)
5013000000 562
0.5%
5011000000 1289
1.1%
5000000000 46
 
< 0.1%
4889000000 185
 
0.2%
4888000000 207
 
0.2%

SIGNGU_NM
Text

Distinct206
Distinct (%)0.2%
Missing1055
Missing (%)0.9%
Memory size956.2 KiB
2023-07-11T01:17:03.204854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.94527966
Min length2

Characters and Unicode

Total characters357339
Distinct characters131
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row김천시
2nd row안산시
3rd row김천시
4th row안산시
5th row성남시
ValueCountFrequency (%)
서구 3419
 
2.8%
북구 2980
 
2.5%
남구 2781
 
2.3%
수원시 2681
 
2.2%
청주시 2570
 
2.1%
고양시 2263
 
1.9%
중구 2030
 
1.7%
화성시 2010
 
1.7%
강남구 1997
 
1.6%
동구 1993
 
1.6%
Other values (196) 96602
79.6%
2023-07-11T01:17:03.747065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 65304
18.3%
구 44829
 
12.5%
주 16205
 
4.5%
군 16113
 
4.5%
천 11169
 
3.1%
양 10175
 
2.8%
남 9986
 
2.8%
산 9492
 
2.7%
성 9319
 
2.6%
서 8567
 
2.4%
Other values (121) 156180
43.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 357339
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 65304
18.3%
구 44829
 
12.5%
주 16205
 
4.5%
군 16113
 
4.5%
천 11169
 
3.1%
양 10175
 
2.8%
남 9986
 
2.8%
산 9492
 
2.7%
성 9319
 
2.6%
서 8567
 
2.4%
Other values (121) 156180
43.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 357339
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 65304
18.3%
구 44829
 
12.5%
주 16205
 
4.5%
군 16113
 
4.5%
천 11169
 
3.1%
양 10175
 
2.8%
남 9986
 
2.8%
산 9492
 
2.7%
성 9319
 
2.6%
서 8567
 
2.4%
Other values (121) 156180
43.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 357339
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 65304
18.3%
구 44829
 
12.5%
주 16205
 
4.5%
군 16113
 
4.5%
천 11169
 
3.1%
양 10175
 
2.8%
남 9986
 
2.8%
산 9492
 
2.7%
성 9319
 
2.6%
서 8567
 
2.4%
Other values (121) 156180
43.7%

FCLTY_MANAGE_CTPRVN_CD
Text

MISSING 

Distinct19
Distinct (%)< 0.1%
Missing26464
Missing (%)21.6%
Memory size956.2 KiB
2023-07-11T01:17:03.913054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters959170
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4700000000
2nd row4100000000
3rd row4700000000
4th row4100000000
5th row4100000000
ValueCountFrequency (%)
4100000000 23637
24.6%
1100000000 11300
11.8%
4700000000 9543
9.9%
4800000000 6845
 
7.1%
4600000000 5888
 
6.1%
2600000000 4759
 
5.0%
4300000000 4710
 
4.9%
4200000000 4696
 
4.9%
4400000000 3916
 
4.1%
2800000000 3737
 
3.9%
Other values (9) 16886
17.6%
2023-07-11T01:17:04.165066image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 770850
80.4%
4 66607
 
6.9%
1 48449
 
5.1%
2 19065
 
2.0%
7 12889
 
1.3%
6 11293
 
1.2%
8 10582
 
1.1%
3 9628
 
1.0%
5 4910
 
0.5%
9 2527
 
0.3%
Other values (2) 2370
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 956800
99.8%
Lowercase Letter 2370
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 770850
80.6%
4 66607
 
7.0%
1 48449
 
5.1%
2 19065
 
2.0%
7 12889
 
1.3%
6 11293
 
1.2%
8 10582
 
1.1%
3 9628
 
1.0%
5 4910
 
0.5%
9 2527
 
0.3%
Lowercase Letter
ValueCountFrequency (%)
u 1185
50.0%
n 1185
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 956800
99.8%
Latin 2370
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 770850
80.6%
4 66607
 
7.0%
1 48449
 
5.1%
2 19065
 
2.0%
7 12889
 
1.3%
6 11293
 
1.2%
8 10582
 
1.1%
3 9628
 
1.0%
5 4910
 
0.5%
9 2527
 
0.3%
Latin
ValueCountFrequency (%)
u 1185
50.0%
n 1185
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 959170
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 770850
80.4%
4 66607
 
6.9%
1 48449
 
5.1%
2 19065
 
2.0%
7 12889
 
1.3%
6 11293
 
1.2%
8 10582
 
1.1%
3 9628
 
1.0%
5 4910
 
0.5%
9 2527
 
0.3%
Other values (2) 2370
 
0.2%

FCLTY_MANAGE_CTPRVN_NM
Text

MISSING 

Distinct17
Distinct (%)< 0.1%
Missing27697
Missing (%)22.6%
Memory size956.2 KiB
2023-07-11T01:17:04.334766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length7
Median length5
Mean length4.081999071
Min length3

Characters and Unicode

Total characters386500
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경상북도
2nd row경기도
3rd row경상북도
4th row경기도
5th row경기도
ValueCountFrequency (%)
경기도 23637
25.0%
서울특별시 11300
11.9%
경상북도 9543
10.1%
경상남도 6845
 
7.2%
전라남도 5888
 
6.2%
부산광역시 4759
 
5.0%
충청북도 4710
 
5.0%
강원도 4696
 
5.0%
충청남도 3916
 
4.1%
인천광역시 3737
 
3.9%
Other values (7) 15653
16.5%
2023-07-11T01:17:04.645721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
도 64145
16.6%
경 40025
 
10.4%
시 30539
 
7.9%
기 23637
 
6.1%
광 21168
 
5.5%
역 18641
 
4.8%
북 17709
 
4.6%
남 16649
 
4.3%
상 16388
 
4.2%
울 13464
 
3.5%
Other values (21) 124135
32.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 386500
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 64145
16.6%
경 40025
 
10.4%
시 30539
 
7.9%
기 23637
 
6.1%
광 21168
 
5.5%
역 18641
 
4.8%
북 17709
 
4.6%
남 16649
 
4.3%
상 16388
 
4.2%
울 13464
 
3.5%
Other values (21) 124135
32.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 386500
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 64145
16.6%
경 40025
 
10.4%
시 30539
 
7.9%
기 23637
 
6.1%
광 21168
 
5.5%
역 18641
 
4.8%
북 17709
 
4.6%
남 16649
 
4.3%
상 16388
 
4.2%
울 13464
 
3.5%
Other values (21) 124135
32.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 386500
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
도 64145
16.6%
경 40025
 
10.4%
시 30539
 
7.9%
기 23637
 
6.1%
광 21168
 
5.5%
역 18641
 
4.8%
북 17709
 
4.6%
남 16649
 
4.3%
상 16388
 
4.2%
울 13464
 
3.5%
Other values (21) 124135
32.1%

FCLTY_MANAGE_SIGNGU_CD
Real number (ℝ)

MISSING 

Distinct272
Distinct (%)0.3%
Missing26957
Missing (%)22.0%
Infinite0
Infinite (%)0.0%
Mean3725508760
Minimum1111000000
Maximum5013000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size956.2 KiB
2023-07-11T01:17:04.773206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1111000000
5-th percentile1147000000
Q12915500000
median4148000000
Q34611000000
95-th percentile4831000000
Maximum5013000000
Range3902000000
Interquartile range (IQR)1695500000

Descriptive statistics

Standard deviation1156068745
Coefficient of variation (CV)0.3103116431
Kurtosis0.1833269854
Mean3725508760
Median Absolute Deviation (MAD)563100000
Skewness-1.168675291
Sum3.555029479 × 1014
Variance1.336494943 × 1018
MonotonicityNot monotonic
2023-07-11T01:17:04.892485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4159000000 1694
 
1.4%
4119000000 1555
 
1.3%
4113000000 1436
 
1.2%
4139000000 1298
 
1.1%
4122000000 1128
 
0.9%
4311000000 1117
 
0.9%
1171000000 1092
 
0.9%
4136000000 1064
 
0.9%
4719000000 1059
 
0.9%
2826000000 1051
 
0.9%
Other values (262) 82930
67.8%
(Missing) 26957
 
22.0%
ValueCountFrequency (%)
1111000000 268
0.2%
1114000000 313
0.3%
1117000000 268
0.2%
1120000000 230
0.2%
1121000000 130
0.1%
ValueCountFrequency (%)
5013000000 574
0.5%
5011000000 900
0.7%
4889000000 185
 
0.2%
4888000000 176
 
0.1%
4887000000 135
 
0.1%

FCLTY_MANAGE_SIGNGU_NM
Text

MISSING 

Distinct238
Distinct (%)0.3%
Missing28916
Missing (%)23.6%
Memory size956.2 KiB
2023-07-11T01:17:05.393148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.16079816
Min length2

Characters and Unicode

Total characters295424
Distinct characters141
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row김천시
2nd row안산시 단원구
3rd row김천시
4th row안산시 상록구
5th row성남시
ValueCountFrequency (%)
서구 2741
 
2.8%
북구 2505
 
2.5%
남구 1972
 
2.0%
청주시 1966
 
2.0%
화성시 1693
 
1.7%
성남시 1685
 
1.7%
동구 1674
 
1.7%
수원시 1658
 
1.7%
부천시 1542
 
1.6%
고양시 1488
 
1.5%
Other values (226) 79485
80.8%
2023-07-11T01:17:05.946011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 52963
17.9%
구 34433
 
11.7%
군 15449
 
5.2%
주 12939
 
4.4%
천 9332
 
3.2%
산 9201
 
3.1%
양 8115
 
2.7%
성 7685
 
2.6%
서 7469
 
2.5%
남 7051
 
2.4%
Other values (131) 130787
44.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 290480
98.3%
Space Separator 4944
 
1.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 52963
18.2%
구 34433
 
11.9%
군 15449
 
5.3%
주 12939
 
4.5%
천 9332
 
3.2%
산 9201
 
3.2%
양 8115
 
2.8%
성 7685
 
2.6%
서 7469
 
2.6%
남 7051
 
2.4%
Other values (130) 125843
43.3%
Space Separator
ValueCountFrequency (%)
4944
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 290480
98.3%
Common 4944
 
1.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 52963
18.2%
구 34433
 
11.9%
군 15449
 
5.3%
주 12939
 
4.5%
천 9332
 
3.2%
산 9201
 
3.2%
양 8115
 
2.8%
성 7685
 
2.6%
서 7469
 
2.6%
남 7051
 
2.4%
Other values (130) 125843
43.3%
Common
ValueCountFrequency (%)
4944
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 290480
98.3%
ASCII 4944
 
1.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 52963
18.2%
구 34433
 
11.9%
군 15449
 
5.3%
주 12939
 
4.5%
천 9332
 
3.2%
산 9201
 
3.2%
양 8115
 
2.8%
성 7685
 
2.6%
서 7469
 
2.6%
남 7051
 
2.4%
Other values (130) 125843
43.3%
ASCII
ValueCountFrequency (%)
4944
100.0%

FCLTY_MANAGE_EMD_CD
Real number (ℝ)

MISSING 

Distinct8406
Distinct (%)12.7%
Missing55932
Missing (%)45.7%
Infinite0
Infinite (%)0.0%
Mean3655923873
Minimum1111010100
Maximum5013032025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size956.2 KiB
2023-07-11T01:17:06.039272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1111010100
5-th percentile1141011700
Q12823710700
median4139011900
Q34518010100
95-th percentile4827033025
Maximum5013032025
Range3902021925
Interquartile range (IQR)1694299400

Descriptive statistics

Standard deviation1187476287
Coefficient of variation (CV)0.3248088111
Kurtosis-0.1025703609
Mean3655923873
Median Absolute Deviation (MAD)576000100
Skewness-1.069437876
Sum2.429324854 × 1014
Variance1.410099931 × 1018
MonotonicityNot monotonic
2023-07-11T01:17:06.161769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4139013200 452
 
0.4%
4119010900 266
 
0.2%
4117110100 252
 
0.2%
4119010800 229
 
0.2%
4117310400 202
 
0.2%
2818510600 196
 
0.2%
2647010200 191
 
0.2%
4117310200 178
 
0.1%
1153010200 175
 
0.1%
1171011100 170
 
0.1%
Other values (8396) 64138
52.4%
(Missing) 55932
45.7%
ValueCountFrequency (%)
1111010100 1
< 0.1%
1111010200 1
< 0.1%
1111010600 1
< 0.1%
1111010700 2
< 0.1%
1111010800 1
< 0.1%
ValueCountFrequency (%)
5013032025 4
< 0.1%
5013032024 1
 
< 0.1%
5013032023 2
< 0.1%
5013032022 3
< 0.1%
5013032021 4
< 0.1%

FCLTY_MANAGE_EMD_NM
Text

MISSING 

Distinct5627
Distinct (%)8.6%
Missing56929
Missing (%)46.5%
Memory size956.2 KiB
2023-07-11T01:17:06.704539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.012864389
Min length2

Characters and Unicode

Total characters197198
Distinct characters378
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1340 ?
Unique (%)2.0%

Sample

1st row원시동
2nd row성내동
3rd row본오동
4th row사동
5th row송계리
ValueCountFrequency (%)
정왕동 452
 
0.7%
중동 448
 
0.7%
상동 373
 
0.6%
안양동 252
 
0.4%
신천동 250
 
0.4%
호계동 228
 
0.3%
신정동 224
 
0.3%
송도동 206
 
0.3%
연산동 197
 
0.3%
중산동 192
 
0.3%
Other values (5617) 62630
95.7%
2023-07-11T01:17:07.281150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 48092
24.4%
리 19394
 
9.8%
산 4896
 
2.5%
정 3483
 
1.8%
신 3460
 
1.8%
곡 2990
 
1.5%
천 2743
 
1.4%
가 2732
 
1.4%
대 2359
 
1.2%
성 2033
 
1.0%
Other values (368) 105016
53.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 195784
99.3%
Decimal Number 1408
 
0.7%
Close Punctuation 3
 
< 0.1%
Open Punctuation 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 48092
24.6%
리 19394
 
9.9%
산 4896
 
2.5%
정 3483
 
1.8%
신 3460
 
1.8%
곡 2990
 
1.5%
천 2743
 
1.4%
가 2732
 
1.4%
대 2359
 
1.2%
성 2033
 
1.0%
Other values (358) 103602
52.9%
Decimal Number
ValueCountFrequency (%)
2 449
31.9%
1 444
31.5%
3 282
20.0%
4 91
 
6.5%
5 73
 
5.2%
6 42
 
3.0%
7 19
 
1.3%
8 8
 
0.6%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 195778
99.3%
Common 1414
 
0.7%
Han 6
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 48092
24.6%
리 19394
 
9.9%
산 4896
 
2.5%
정 3483
 
1.8%
신 3460
 
1.8%
곡 2990
 
1.5%
천 2743
 
1.4%
가 2732
 
1.4%
대 2359
 
1.2%
성 2033
 
1.0%
Other values (353) 103596
52.9%
Common
ValueCountFrequency (%)
2 449
31.8%
1 444
31.4%
3 282
19.9%
4 91
 
6.4%
5 73
 
5.2%
6 42
 
3.0%
7 19
 
1.3%
8 8
 
0.6%
) 3
 
0.2%
( 3
 
0.2%
Han
ValueCountFrequency (%)
沙 2
33.3%
岐 1
16.7%
坪 1
16.7%
平 1
16.7%
岩 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 195778
99.3%
ASCII 1414
 
0.7%
CJK 6
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 48092
24.6%
리 19394
 
9.9%
산 4896
 
2.5%
정 3483
 
1.8%
신 3460
 
1.8%
곡 2990
 
1.5%
천 2743
 
1.4%
가 2732
 
1.4%
대 2359
 
1.2%
성 2033
 
1.0%
Other values (353) 103596
52.9%
ASCII
ValueCountFrequency (%)
2 449
31.8%
1 444
31.4%
3 282
19.9%
4 91
 
6.4%
5 73
 
5.2%
6 42
 
3.0%
7 19
 
1.3%
8 8
 
0.6%
) 3
 
0.2%
( 3
 
0.2%
CJK
ValueCountFrequency (%)
沙 2
33.3%
岐 1
16.7%
坪 1
16.7%
平 1
16.7%
岩 1
16.7%

FCLTY_MANAGE_LI_CD
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing122381
Missing (%)100.0%
Memory size956.2 KiB

FCLTY_MANAGE_LI_NM
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing122381
Missing (%)100.0%
Memory size956.2 KiB

FCLTY_OPER_STLE_VALUE
Text

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing62662
Missing (%)51.2%
Memory size956.2 KiB
2023-07-11T01:17:07.392393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters238876
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row자체운영
2nd row자체운영
3rd row자체운영
4th row자체운영
5th row자체운영
ValueCountFrequency (%)
자체운영 57412
96.1%
위탁운영 2307
 
3.9%
2023-07-11T01:17:07.597804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
운 59719
25.0%
영 59719
25.0%
자 57412
24.0%
체 57412
24.0%
위 2307
 
1.0%
탁 2307
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 238876
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
운 59719
25.0%
영 59719
25.0%
자 57412
24.0%
체 57412
24.0%
위 2307
 
1.0%
탁 2307
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 238876
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
운 59719
25.0%
영 59719
25.0%
자 57412
24.0%
체 57412
24.0%
위 2307
 
1.0%
탁 2307
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 238876
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
운 59719
25.0%
영 59719
25.0%
자 57412
24.0%
체 57412
24.0%
위 2307
 
1.0%
탁 2307
 
1.0%

POSESN_MBY_CD
Real number (ℝ)

MISSING  SKEWED 

Distinct19
Distinct (%)< 0.1%
Missing82451
Missing (%)67.4%
Infinite0
Infinite (%)0.0%
Mean1.028199349
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size956.2 KiB
2023-07-11T01:17:07.682405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum21
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5698970763
Coefficient of variation (CV)0.5542671049
Kurtosis787.3392626
Mean1.028199349
Median Absolute Deviation (MAD)0
Skewness26.44124885
Sum41056
Variance0.3247826776
MonotonicityNot monotonic
2023-07-11T01:17:07.762010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1 39768
32.5%
3 30
 
< 0.1%
4 29
 
< 0.1%
10 14
 
< 0.1%
7 14
 
< 0.1%
5 11
 
< 0.1%
8 10
 
< 0.1%
9 9
 
< 0.1%
21 9
 
< 0.1%
6 8
 
< 0.1%
Other values (9) 28
 
< 0.1%
(Missing) 82451
67.4%
ValueCountFrequency (%)
1 39768
32.5%
2 6
 
< 0.1%
3 30
 
< 0.1%
4 29
 
< 0.1%
5 11
 
< 0.1%
ValueCountFrequency (%)
21 9
< 0.1%
20 5
< 0.1%
19 3
 
< 0.1%
18 5
< 0.1%
16 5
< 0.1%

POSESN_MBY_NM
Text

MISSING 

Distinct5
Distinct (%)< 0.1%
Missing82537
Missing (%)67.4%
Memory size956.2 KiB
2023-07-11T01:17:07.856464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length8
Median length3
Mean length3.003588997
Min length3

Characters and Unicode

Total characters119675
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row지자체
2nd row지자체
3rd row지자체
4th row지자체
5th row지자체
ValueCountFrequency (%)
지자체 39768
99.8%
국민체육 30
 
0.1%
대한체육회 29
 
0.1%
대한장애인체육회 11
 
< 0.1%
문체부 6
 
< 0.1%
2023-07-11T01:17:08.029211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
체 39844
33.3%
지 39768
33.2%
자 39768
33.2%
육 70
 
0.1%
대 40
 
< 0.1%
한 40
 
< 0.1%
회 40
 
< 0.1%
국 30
 
< 0.1%
민 30
 
< 0.1%
장 11
 
< 0.1%
Other values (4) 34
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 119675
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
체 39844
33.3%
지 39768
33.2%
자 39768
33.2%
육 70
 
0.1%
대 40
 
< 0.1%
한 40
 
< 0.1%
회 40
 
< 0.1%
국 30
 
< 0.1%
민 30
 
< 0.1%
장 11
 
< 0.1%
Other values (4) 34
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 119675
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
체 39844
33.3%
지 39768
33.2%
자 39768
33.2%
육 70
 
0.1%
대 40
 
< 0.1%
한 40
 
< 0.1%
회 40
 
< 0.1%
국 30
 
< 0.1%
민 30
 
< 0.1%
장 11
 
< 0.1%
Other values (4) 34
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 119675
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
체 39844
33.3%
지 39768
33.2%
자 39768
33.2%
육 70
 
0.1%
대 40
 
< 0.1%
한 40
 
< 0.1%
회 40
 
< 0.1%
국 30
 
< 0.1%
민 30
 
< 0.1%
장 11
 
< 0.1%
Other values (4) 34
 
< 0.1%

POSESN_MBY_CTPRVN_CD
Real number (ℝ)

MISSING 

Distinct17
Distinct (%)< 0.1%
Missing45539
Missing (%)37.2%
Infinite0
Infinite (%)0.0%
Mean3614546732
Minimum1100000000
Maximum5000000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size956.2 KiB
2023-07-11T01:17:08.119438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1100000000
5-th percentile1100000000
Q12800000000
median4100000000
Q34600000000
95-th percentile4800000000
Maximum5000000000
Range3900000000
Interquartile range (IQR)1800000000

Descriptive statistics

Standard deviation1240458330
Coefficient of variation (CV)0.3431850303
Kurtosis-0.2728237484
Mean3614546732
Median Absolute Deviation (MAD)600000000
Skewness-1.033022866
Sum2.77749 × 1014
Variance1.538736868 × 1018
MonotonicityNot monotonic
2023-07-11T01:17:08.217694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
4100000000 15907
 
13.0%
1100000000 11257
 
9.2%
4700000000 9014
 
7.4%
4800000000 5986
 
4.9%
4600000000 4873
 
4.0%
2800000000 3703
 
3.0%
4200000000 3694
 
3.0%
4300000000 3656
 
3.0%
2600000000 3473
 
2.8%
4500000000 3075
 
2.5%
Other values (7) 12204
 
10.0%
(Missing) 45539
37.2%
ValueCountFrequency (%)
1100000000 11257
9.2%
2600000000 3473
 
2.8%
2700000000 2508
 
2.0%
2800000000 3703
 
3.0%
2900000000 1944
 
1.6%
ValueCountFrequency (%)
5000000000 1126
 
0.9%
4800000000 5986
4.9%
4700000000 9014
7.4%
4600000000 4873
4.0%
4500000000 3075
 
2.5%

POSESN_MBY_CTPRVN_NM
Text

MISSING 

Distinct17
Distinct (%)< 0.1%
Missing45539
Missing (%)37.2%
Memory size956.2 KiB
2023-07-11T01:17:08.384887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length7
Median length5
Mean length4.143528279
Min length3

Characters and Unicode

Total characters318397
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경상북도
2nd row경기도
3rd row경상북도
4th row경기도
5th row경기도
ValueCountFrequency (%)
경기도 15907
20.7%
서울특별시 11257
14.6%
경상북도 9014
11.7%
경상남도 5986
 
7.8%
전라남도 4873
 
6.3%
인천광역시 3703
 
4.8%
강원도 3694
 
4.8%
충청북도 3656
 
4.8%
부산광역시 3473
 
4.5%
전라북도 3075
 
4.0%
Other values (7) 12204
15.9%
2023-07-11T01:17:08.683878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
도 50122
15.7%
경 30907
 
9.7%
시 26720
 
8.4%
광 17141
 
5.4%
기 15907
 
5.0%
북 15745
 
4.9%
역 15197
 
4.8%
상 15000
 
4.7%
남 13650
 
4.3%
울 13034
 
4.1%
Other values (21) 104974
33.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 318397
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 50122
15.7%
경 30907
 
9.7%
시 26720
 
8.4%
광 17141
 
5.4%
기 15907
 
5.0%
북 15745
 
4.9%
역 15197
 
4.8%
상 15000
 
4.7%
남 13650
 
4.3%
울 13034
 
4.1%
Other values (21) 104974
33.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 318397
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 50122
15.7%
경 30907
 
9.7%
시 26720
 
8.4%
광 17141
 
5.4%
기 15907
 
5.0%
북 15745
 
4.9%
역 15197
 
4.8%
상 15000
 
4.7%
남 13650
 
4.3%
울 13034
 
4.1%
Other values (21) 104974
33.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 318397
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
도 50122
15.7%
경 30907
 
9.7%
시 26720
 
8.4%
광 17141
 
5.4%
기 15907
 
5.0%
북 15745
 
4.9%
역 15197
 
4.8%
상 15000
 
4.7%
남 13650
 
4.3%
울 13034
 
4.1%
Other values (21) 104974
33.0%

POSESN_MBY_SIGNGU_CD
Real number (ℝ)

MISSING 

Distinct272
Distinct (%)0.4%
Missing45539
Missing (%)37.2%
Infinite0
Infinite (%)0.0%
Mean3649903372
Minimum1100000000
Maximum5013000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size956.2 KiB
2023-07-11T01:17:08.802559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1100000000
5-th percentile1138000000
Q12823700000
median4148000000
Q34615000000
95-th percentile4831000000
Maximum5013000000
Range3913000000
Interquartile range (IQR)1791300000

Descriptive statistics

Standard deviation1238489583
Coefficient of variation (CV)0.3393211976
Kurtosis-0.2981400951
Mean3649903372
Median Absolute Deviation (MAD)573000000
Skewness-1.021860784
Sum2.804658749 × 1014
Variance1.533856448 × 1018
MonotonicityNot monotonic
2023-07-11T01:17:08.913591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4311000000 1622
 
1.3%
4111000000 1509
 
1.2%
1168000000 1291
 
1.1%
4159000000 1285
 
1.0%
4128000000 1272
 
1.0%
4812000000 1098
 
0.9%
4511000000 1069
 
0.9%
4711000000 987
 
0.8%
4119000000 970
 
0.8%
4113000000 926
 
0.8%
Other values (262) 64813
53.0%
(Missing) 45539
37.2%
ValueCountFrequency (%)
1100000000 139
0.1%
1111000000 324
0.3%
1114000000 315
0.3%
1117000000 215
0.2%
1120000000 328
0.3%
ValueCountFrequency (%)
5013000000 396
0.3%
5011000000 716
0.6%
5000000000 14
 
< 0.1%
4889000000 166
 
0.1%
4888000000 168
 
0.1%

POSESN_MBY_SIGNGU_NM
Text

MISSING 

Distinct232
Distinct (%)0.3%
Missing46304
Missing (%)37.8%
Memory size956.2 KiB
2023-07-11T01:17:09.392975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length9
Median length3
Mean length2.967651195
Min length2

Characters and Unicode

Total characters225770
Distinct characters140
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)< 0.1%

Sample

1st row김천시
2nd row안산시
3rd row김천시
4th row안산시
5th row성남시
ValueCountFrequency (%)
서구 1921
 
2.5%
남구 1871
 
2.4%
북구 1730
 
2.3%
청주시 1623
 
2.1%
수원시 1576
 
2.1%
강남구 1291
 
1.7%
화성시 1285
 
1.7%
고양시 1283
 
1.7%
중구 1142
 
1.5%
동구 1115
 
1.5%
Other values (220) 61613
80.6%
2023-07-11T01:17:10.007461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 40295
17.8%
구 26555
 
11.8%
군 12537
 
5.6%
주 10613
 
4.7%
천 7188
 
3.2%
양 6216
 
2.8%
남 6135
 
2.7%
산 6127
 
2.7%
성 5805
 
2.6%
서 5067
 
2.2%
Other values (130) 99232
44.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 225397
99.8%
Space Separator 373
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 40295
17.9%
구 26555
 
11.8%
군 12537
 
5.6%
주 10613
 
4.7%
천 7188
 
3.2%
양 6216
 
2.8%
남 6135
 
2.7%
산 6127
 
2.7%
성 5805
 
2.6%
서 5067
 
2.2%
Other values (129) 98859
43.9%
Space Separator
ValueCountFrequency (%)
373
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 225397
99.8%
Common 373
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 40295
17.9%
구 26555
 
11.8%
군 12537
 
5.6%
주 10613
 
4.7%
천 7188
 
3.2%
양 6216
 
2.8%
남 6135
 
2.7%
산 6127
 
2.7%
성 5805
 
2.6%
서 5067
 
2.2%
Other values (129) 98859
43.9%
Common
ValueCountFrequency (%)
373
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 225397
99.8%
ASCII 373
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 40295
17.9%
구 26555
 
11.8%
군 12537
 
5.6%
주 10613
 
4.7%
천 7188
 
3.2%
양 6216
 
2.8%
남 6135
 
2.7%
산 6127
 
2.7%
성 5805
 
2.6%
서 5067
 
2.2%
Other values (129) 98859
43.9%
ASCII
ValueCountFrequency (%)
373
100.0%

RSPNSBLTY_NM
Text

MISSING 

Distinct960
Distinct (%)4.1%
Missing98918
Missing (%)80.8%
Memory size956.2 KiB
2023-07-11T01:17:10.434792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length19
Median length18
Mean length6.076929634
Min length1

Characters and Unicode

Total characters142583
Distinct characters319
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique306 ?
Unique (%)1.3%

Sample

1st row스포츠산업과
2nd row스포츠산업과
3rd row스포츠산업과
4th row문화관광과
5th row스포츠산업과
ValueCountFrequency (%)
체육진흥과 2802
 
10.6%
문화체육과 1241
 
4.7%
스포츠산업과 1081
 
4.1%
문화관광과 991
 
3.7%
체육시설팀 950
 
3.6%
체육시설사업소 839
 
3.2%
공원녹지과 798
 
3.0%
체육지원과 759
 
2.9%
새마을체육과 666
 
2.5%
시설관리팀 582
 
2.2%
Other values (945) 15848
59.7%
2023-07-11T01:17:10.935562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
과 14754
 
10.3%
육 14066
 
9.9%
체 13236
 
9.3%
시 4821
 
3.4%
설 4371
 
3.1%
화 4283
 
3.0%
문 4166
 
2.9%
업 3865
 
2.7%
관 3764
 
2.6%
소 3756
 
2.6%
Other values (309) 71501
50.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 138561
97.2%
Space Separator 3096
 
2.2%
Other Punctuation 419
 
0.3%
Decimal Number 261
 
0.2%
Open Punctuation 95
 
0.1%
Close Punctuation 92
 
0.1%
Uppercase Letter 38
 
< 0.1%
Dash Punctuation 17
 
< 0.1%
Lowercase Letter 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
과 14754
 
10.6%
육 14066
 
10.2%
체 13236
 
9.6%
시 4821
 
3.5%
설 4371
 
3.2%
화 4283
 
3.1%
문 4166
 
3.0%
업 3865
 
2.8%
관 3764
 
2.7%
소 3756
 
2.7%
Other values (285) 67479
48.7%
Decimal Number
ValueCountFrequency (%)
1 94
36.0%
2 78
29.9%
3 32
 
12.3%
4 28
 
10.7%
5 19
 
7.3%
7 6
 
2.3%
0 2
 
0.8%
6 2
 
0.8%
Uppercase Letter
ValueCountFrequency (%)
Y 9
23.7%
A 9
23.7%
C 9
23.7%
M 5
13.2%
W 4
10.5%
T 1
 
2.6%
F 1
 
2.6%
Lowercase Letter
ValueCountFrequency (%)
y 1
25.0%
w 1
25.0%
c 1
25.0%
a 1
25.0%
Space Separator
ValueCountFrequency (%)
3096
100.0%
Other Punctuation
ValueCountFrequency (%)
, 419
100.0%
Open Punctuation
ValueCountFrequency (%)
( 95
100.0%
Close Punctuation
ValueCountFrequency (%)
) 92
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 138561
97.2%
Common 3980
 
2.8%
Latin 42
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
과 14754
 
10.6%
육 14066
 
10.2%
체 13236
 
9.6%
시 4821
 
3.5%
설 4371
 
3.2%
화 4283
 
3.1%
문 4166
 
3.0%
업 3865
 
2.8%
관 3764
 
2.7%
소 3756
 
2.7%
Other values (285) 67479
48.7%
Common
ValueCountFrequency (%)
3096
77.8%
, 419
 
10.5%
( 95
 
2.4%
1 94
 
2.4%
) 92
 
2.3%
2 78
 
2.0%
3 32
 
0.8%
4 28
 
0.7%
5 19
 
0.5%
- 17
 
0.4%
Other values (3) 10
 
0.3%
Latin
ValueCountFrequency (%)
Y 9
21.4%
A 9
21.4%
C 9
21.4%
M 5
11.9%
W 4
9.5%
y 1
 
2.4%
w 1
 
2.4%
c 1
 
2.4%
a 1
 
2.4%
T 1
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 138560
97.2%
ASCII 4022
 
2.8%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
과 14754
 
10.6%
육 14066
 
10.2%
체 13236
 
9.6%
시 4821
 
3.5%
설 4371
 
3.2%
화 4283
 
3.1%
문 4166
 
3.0%
업 3865
 
2.8%
관 3764
 
2.7%
소 3756
 
2.7%
Other values (284) 67478
48.7%
ASCII
ValueCountFrequency (%)
3096
77.0%
, 419
 
10.4%
( 95
 
2.4%
1 94
 
2.3%
) 92
 
2.3%
2 78
 
1.9%
3 32
 
0.8%
4 28
 
0.7%
5 19
 
0.5%
- 17
 
0.4%
Other values (14) 52
 
1.3%
Compat Jamo
ValueCountFrequency (%)
ㅛ 1
100.0%

RSPNSBLTY_TEL_NO
Text

MISSING 

Distinct22033
Distinct (%)46.3%
Missing74812
Missing (%)61.1%
Memory size956.2 KiB
2023-07-11T01:17:11.217789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.52950451
Min length7

Characters and Unicode

Total characters548447
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19831 ?
Unique (%)41.7%

Sample

1st row054-420-8033
2nd row031-492-3391
3rd row054-420-8077
4th row054-420-8031
5th row055-960-4462
ValueCountFrequency (%)
054-639-3813 475
 
1.0%
054-840-5506 467
 
1.0%
061-749-6662 359
 
0.8%
061-659-3269 322
 
0.7%
032-625-2501 317
 
0.7%
054-537-7037 296
 
0.6%
054-270-2804 292
 
0.6%
031-5189-6188 286
 
0.6%
055-359-5448 283
 
0.6%
055-650-4723 257
 
0.5%
Other values (22023) 44215
92.9%
2023-07-11T01:17:11.632864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 89391
16.3%
0 80678
14.7%
3 58226
10.6%
5 53956
9.8%
2 47975
8.7%
4 47252
8.6%
1 39874
7.3%
6 39640
7.2%
7 33589
 
6.1%
8 31416
 
5.7%
Other values (3) 26450
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 458851
83.7%
Dash Punctuation 89391
 
16.3%
Close Punctuation 197
 
< 0.1%
Open Punctuation 8
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 80678
17.6%
3 58226
12.7%
5 53956
11.8%
2 47975
10.5%
4 47252
10.3%
1 39874
8.7%
6 39640
8.6%
7 33589
7.3%
8 31416
 
6.8%
9 26245
 
5.7%
Dash Punctuation
ValueCountFrequency (%)
- 89391
100.0%
Close Punctuation
ValueCountFrequency (%)
) 197
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 548447
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 89391
16.3%
0 80678
14.7%
3 58226
10.6%
5 53956
9.8%
2 47975
8.7%
4 47252
8.6%
1 39874
7.3%
6 39640
7.2%
7 33589
 
6.1%
8 31416
 
5.7%
Other values (3) 26450
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 548447
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 89391
16.3%
0 80678
14.7%
3 58226
10.6%
5 53956
9.8%
2 47975
8.7%
4 47252
8.6%
1 39874
7.3%
6 39640
7.2%
7 33589
 
6.1%
8 31416
 
5.7%
Other values (3) 26450
 
4.8%

NDOR_SDIV_NM
Text

MISSING 

Distinct4
Distinct (%)< 0.1%
Missing47164
Missing (%)38.5%
Memory size956.2 KiB
2023-07-11T01:17:11.759479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.027905926
Min length2

Characters and Unicode

Total characters152533
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row실내
2nd row실내
3rd row실내
4th row실내
5th row실내
ValueCountFrequency (%)
실내 41429
55.1%
없음 28158
37.4%
실외 3531
 
4.7%
실내외 2099
 
2.8%
2023-07-11T01:17:11.953995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
실 47059
30.9%
내 43528
28.5%
없 28158
18.5%
음 28158
18.5%
외 5630
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 152533
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
실 47059
30.9%
내 43528
28.5%
없 28158
18.5%
음 28158
18.5%
외 5630
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 152533
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
실 47059
30.9%
내 43528
28.5%
없 28158
18.5%
음 28158
18.5%
외 5630
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 152533
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
실 47059
30.9%
내 43528
28.5%
없 28158
18.5%
음 28158
18.5%
외 5630
 
3.7%

ADTM_CO
Real number (ℝ)

MISSING 

Distinct614
Distinct (%)41.6%
Missing120904
Missing (%)98.8%
Infinite0
Infinite (%)0.0%
Mean3089.47258
Minimum0
Maximum118351
Zeros8
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size956.2 KiB
2023-07-11T01:17:12.041054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile50
Q1200
median500
Q31988
95-th percentile15095.6
Maximum118351
Range118351
Interquartile range (IQR)1788

Descriptive statistics

Standard deviation8082.797904
Coefficient of variation (CV)2.616238758
Kurtosis52.50322826
Mean3089.47258
Median Absolute Deviation (MAD)400
Skewness6.058164153
Sum4563151
Variance65331621.96
MonotonicityNot monotonic
2023-07-11T01:17:12.134569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 88
 
0.1%
500 76
 
0.1%
100 66
 
0.1%
300 58
 
< 0.1%
1000 36
 
< 0.1%
400 35
 
< 0.1%
150 28
 
< 0.1%
50 27
 
< 0.1%
3000 20
 
< 0.1%
1500 16
 
< 0.1%
Other values (604) 1027
 
0.8%
(Missing) 120904
98.8%
ValueCountFrequency (%)
0 8
< 0.1%
4 1
 
< 0.1%
8 2
 
< 0.1%
10 6
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
118351 1
 
< 0.1%
73174 3
< 0.1%
69950 1
 
< 0.1%
66806 1
 
< 0.1%
66422 1
 
< 0.1%

ACMD_NMPR_CO
Real number (ℝ)

MISSING 

Distinct351
Distinct (%)19.9%
Missing120617
Missing (%)98.6%
Infinite0
Infinite (%)0.0%
Mean3392.447846
Minimum0
Maximum120000
Zeros6
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size956.2 KiB
2023-07-11T01:17:12.230579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile80
Q1200
median600
Q32500
95-th percentile19000
Maximum120000
Range120000
Interquartile range (IQR)2300

Descriptive statistics

Standard deviation8258.861822
Coefficient of variation (CV)2.434484537
Kurtosis52.6480977
Mean3392.447846
Median Absolute Deviation (MAD)460
Skewness5.866664763
Sum5984278
Variance68208798.6
MonotonicityNot monotonic
2023-07-11T01:17:12.327316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 148
 
0.1%
500 137
 
0.1%
1000 126
 
0.1%
100 114
 
0.1%
300 109
 
0.1%
3000 68
 
0.1%
2000 66
 
0.1%
400 57
 
< 0.1%
1500 46
 
< 0.1%
5000 45
 
< 0.1%
Other values (341) 848
 
0.7%
(Missing) 120617
98.6%
ValueCountFrequency (%)
0 6
< 0.1%
4 1
 
< 0.1%
10 3
< 0.1%
12 1
 
< 0.1%
15 1
 
< 0.1%
ValueCountFrequency (%)
120000 1
< 0.1%
100000 2
< 0.1%
66806 1
< 0.1%
60000 1
< 0.1%
53600 1
< 0.1%

FCLTY_AR_CO
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct4908
Distinct (%)5.8%
Missing38185
Missing (%)31.2%
Infinite0
Infinite (%)0.0%
Mean3575735279
Minimum0
Maximum3.01062016 × 1014
Zeros2272
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size956.2 KiB
2023-07-11T01:17:12.422158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile77
Q1148
median225
Q3389
95-th percentile1830
Maximum3.01062016 × 1014
Range3.01062016 × 1014
Interquartile range (IQR)241

Descriptive statistics

Standard deviation1.037552869 × 1012
Coefficient of variation (CV)290.1648999
Kurtosis84196
Mean3575735279
Median Absolute Deviation (MAD)97
Skewness290.16547
Sum3.010626075 × 1014
Variance1.076515957 × 1024
MonotonicityNot monotonic
2023-07-11T01:17:12.513129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2272
 
1.9%
165 714
 
0.6%
198 714
 
0.6%
132 679
 
0.6%
148 454
 
0.4%
150 414
 
0.3%
120 396
 
0.3%
99 384
 
0.3%
180 384
 
0.3%
231 381
 
0.3%
Other values (4898) 77404
63.2%
(Missing) 38185
31.2%
ValueCountFrequency (%)
0 2272
1.9%
1 104
 
0.1%
2 3
 
< 0.1%
3 1
 
< 0.1%
4 4
 
< 0.1%
ValueCountFrequency (%)
3.01062016 × 10141
< 0.1%
20160127 1
< 0.1%
20151012 1
< 0.1%
20120314 1
< 0.1%
20090803 1
< 0.1%

LVLH_OPN_AT
Text

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size956.2 KiB
2023-07-11T01:17:12.587611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters122381
Distinct characters2
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
y 49
100.0%
2023-07-11T01:17:12.731867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
122332
> 99.9%
Y 49
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 122332
> 99.9%
Uppercase Letter 49
 
< 0.1%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
122332
100.0%
Uppercase Letter
ValueCountFrequency (%)
Y 49
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 122332
> 99.9%
Latin 49
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
122332
100.0%
Latin
ValueCountFrequency (%)
Y 49
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 122381
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
122332
> 99.9%
Y 49
 
< 0.1%

LVLH_GMNSM_NM
Text

MISSING 

Distinct576
Distinct (%)47.3%
Missing121164
Missing (%)99.0%
Memory size956.2 KiB
2023-07-11T01:17:12.986394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length19
Median length10
Mean length9.16516023
Min length1

Characters and Unicode

Total characters11154
Distinct characters299
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique474 ?
Unique (%)38.9%

Sample

1st row와~스타디움
2nd row3038304932
3rd row4158300151
4th row4108302213
5th row4108302213
ValueCountFrequency (%)
4158300016 31
 
2.5%
3028303643 26
 
2.1%
4128302811 26
 
2.1%
3048301199 25
 
2.1%
4108302213 24
 
2.0%
4118300348 23
 
1.9%
4068301118 22
 
1.8%
4188206007 22
 
1.8%
4078305456 21
 
1.7%
4178300216 21
 
1.7%
Other values (566) 976
80.2%
2023-07-11T01:17:13.341157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1403
 
12.6%
3 1212
 
10.9%
8 918
 
8.2%
1 901
 
8.1%
4 618
 
5.5%
5 455
 
4.1%
2 400
 
3.6%
6 322
 
2.9%
장 291
 
2.6%
7 216
 
1.9%
Other values (289) 4418
39.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6622
59.4%
Other Letter 4514
40.5%
Open Punctuation 7
 
0.1%
Close Punctuation 7
 
0.1%
Uppercase Letter 3
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 291
 
6.4%
체 203
 
4.5%
육 202
 
4.5%
구 131
 
2.9%
관 129
 
2.9%
터 117
 
2.6%
센 116
 
2.6%
트 107
 
2.4%
이 103
 
2.3%
볼 93
 
2.1%
Other values (273) 3022
66.9%
Decimal Number
ValueCountFrequency (%)
0 1403
21.2%
3 1212
18.3%
8 918
13.9%
1 901
13.6%
4 618
9.3%
5 455
 
6.9%
2 400
 
6.0%
6 322
 
4.9%
7 216
 
3.3%
9 177
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
D 1
33.3%
B 1
33.3%
C 1
33.3%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6637
59.5%
Hangul 4514
40.5%
Latin 3
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 291
 
6.4%
체 203
 
4.5%
육 202
 
4.5%
구 131
 
2.9%
관 129
 
2.9%
터 117
 
2.6%
센 116
 
2.6%
트 107
 
2.4%
이 103
 
2.3%
볼 93
 
2.1%
Other values (273) 3022
66.9%
Common
ValueCountFrequency (%)
0 1403
21.1%
3 1212
18.3%
8 918
13.8%
1 901
13.6%
4 618
9.3%
5 455
 
6.9%
2 400
 
6.0%
6 322
 
4.9%
7 216
 
3.3%
9 177
 
2.7%
Other values (3) 15
 
0.2%
Latin
ValueCountFrequency (%)
D 1
33.3%
B 1
33.3%
C 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6640
59.5%
Hangul 4513
40.5%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1403
21.1%
3 1212
18.3%
8 918
13.8%
1 901
13.6%
4 618
9.3%
5 455
 
6.9%
2 400
 
6.0%
6 322
 
4.8%
7 216
 
3.3%
9 177
 
2.7%
Other values (6) 18
 
0.3%
Hangul
ValueCountFrequency (%)
장 291
 
6.4%
체 203
 
4.5%
육 202
 
4.5%
구 131
 
2.9%
관 129
 
2.9%
터 117
 
2.6%
센 116
 
2.6%
트 107
 
2.4%
이 103
 
2.3%
볼 93
 
2.1%
Other values (272) 3021
66.9%
Compat Jamo
ValueCountFrequency (%)
ㆍ 1
100.0%

UTILIIZA_GRP_NM
Text

MISSING 

Distinct526
Distinct (%)62.0%
Missing121533
Missing (%)99.3%
Memory size956.2 KiB
2023-07-11T01:17:13.683811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length24
Median length19
Mean length6.890330189
Min length1

Characters and Unicode

Total characters5843
Distinct characters313
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique387 ?
Unique (%)45.6%

Sample

1st row태백시
2nd row용인게이트볼동호회
3rd row삼호게이트볼클럽
4th row생활체육게이트볼진안군연합회하노분회
5th row삼가 게이트볼회
ValueCountFrequency (%)
시민 29
 
3.2%
주민 23
 
2.5%
시설관리공단 21
 
2.3%
연합회 18
 
2.0%
군민 13
 
1.4%
기초단체 11
 
1.2%
포천시 9
 
1.0%
중구시설관리공단 7
 
0.8%
일반시민 6
 
0.7%
지역주민 6
 
0.7%
Other values (549) 776
84.4%
2023-07-11T01:17:14.120471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 360
 
6.2%
회 211
 
3.6%
구 176
 
3.0%
단 170
 
2.9%
군 149
 
2.6%
공 146
 
2.5%
) 145
 
2.5%
( 143
 
2.4%
민 140
 
2.4%
관 130
 
2.2%
Other values (303) 4073
69.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5387
92.2%
Close Punctuation 145
 
2.5%
Open Punctuation 143
 
2.4%
Space Separator 71
 
1.2%
Other Punctuation 46
 
0.8%
Uppercase Letter 32
 
0.5%
Lowercase Letter 11
 
0.2%
Decimal Number 7
 
0.1%
Other Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 360
 
6.7%
회 211
 
3.9%
구 176
 
3.3%
단 170
 
3.2%
군 149
 
2.8%
공 146
 
2.7%
민 140
 
2.6%
관 130
 
2.4%
육 129
 
2.4%
탁 117
 
2.2%
Other values (274) 3659
67.9%
Uppercase Letter
ValueCountFrequency (%)
C 6
18.8%
A 5
15.6%
M 5
15.6%
Y 5
15.6%
S 3
9.4%
K 3
9.4%
B 2
 
6.2%
L 1
 
3.1%
G 1
 
3.1%
F 1
 
3.1%
Lowercase Letter
ValueCountFrequency (%)
s 3
27.3%
l 2
18.2%
e 1
 
9.1%
w 1
 
9.1%
r 1
 
9.1%
h 1
 
9.1%
n 1
 
9.1%
a 1
 
9.1%
Decimal Number
ValueCountFrequency (%)
2 4
57.1%
4 1
 
14.3%
1 1
 
14.3%
3 1
 
14.3%
Other Punctuation
ValueCountFrequency (%)
, 37
80.4%
: 8
 
17.4%
· 1
 
2.2%
Close Punctuation
ValueCountFrequency (%)
) 145
100.0%
Open Punctuation
ValueCountFrequency (%)
( 143
100.0%
Space Separator
ValueCountFrequency (%)
71
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5387
92.2%
Common 412
 
7.1%
Latin 43
 
0.7%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 360
 
6.7%
회 211
 
3.9%
구 176
 
3.3%
단 170
 
3.2%
군 149
 
2.8%
공 146
 
2.7%
민 140
 
2.6%
관 130
 
2.4%
육 129
 
2.4%
탁 117
 
2.2%
Other values (274) 3659
67.9%
Latin
ValueCountFrequency (%)
C 6
14.0%
A 5
11.6%
M 5
11.6%
Y 5
11.6%
s 3
 
7.0%
S 3
 
7.0%
K 3
 
7.0%
l 2
 
4.7%
B 2
 
4.7%
L 1
 
2.3%
Other values (8) 8
18.6%
Common
ValueCountFrequency (%)
) 145
35.2%
( 143
34.7%
71
17.2%
, 37
 
9.0%
: 8
 
1.9%
2 4
 
1.0%
4 1
 
0.2%
1 1
 
0.2%
· 1
 
0.2%
3 1
 
0.2%
Han
ValueCountFrequency (%)
市 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5386
92.2%
ASCII 454
 
7.8%
None 2
 
< 0.1%
CJK 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 360
 
6.7%
회 211
 
3.9%
구 176
 
3.3%
단 170
 
3.2%
군 149
 
2.8%
공 146
 
2.7%
민 140
 
2.6%
관 130
 
2.4%
육 129
 
2.4%
탁 117
 
2.2%
Other values (273) 3658
67.9%
ASCII
ValueCountFrequency (%)
) 145
31.9%
( 143
31.5%
71
15.6%
, 37
 
8.1%
: 8
 
1.8%
C 6
 
1.3%
A 5
 
1.1%
M 5
 
1.1%
Y 5
 
1.1%
2 4
 
0.9%
Other values (17) 25
 
5.5%
CJK
ValueCountFrequency (%)
市 1
100.0%
None
ValueCountFrequency (%)
㈜ 1
50.0%
· 1
50.0%

FCLTY_CRTN_STDR_DE
Text

Distinct8388
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size956.2 KiB
2023-07-11T01:17:14.510175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters979048
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1842 ?
Unique (%)1.5%

Sample

1st row
2nd row19970829
3rd row
4th row20030214
5th row20161107
ValueCountFrequency (%)
20161231 11583
 
13.0%
19990101 2547
 
2.9%
20161107 1096
 
1.2%
20161227 343
 
0.4%
20140101 310
 
0.3%
20150101 308
 
0.3%
20130101 307
 
0.3%
20160101 302
 
0.3%
20161222 288
 
0.3%
20120101 271
 
0.3%
Other values (8377) 71717
80.5%
2023-07-11T01:17:14.981734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
266472
27.2%
0 207151
21.2%
2 166615
17.0%
1 161221
16.5%
6 34007
 
3.5%
9 33318
 
3.4%
3 33043
 
3.4%
7 22541
 
2.3%
8 19957
 
2.0%
5 17472
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 712576
72.8%
Space Separator 266472
 
27.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 207151
29.1%
2 166615
23.4%
1 161221
22.6%
6 34007
 
4.8%
9 33318
 
4.7%
3 33043
 
4.6%
7 22541
 
3.2%
8 19957
 
2.8%
5 17472
 
2.5%
4 17251
 
2.4%
Space Separator
ValueCountFrequency (%)
266472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 979048
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
266472
27.2%
0 207151
21.2%
2 166615
17.0%
1 161221
16.5%
6 34007
 
3.5%
9 33318
 
3.4%
3 33043
 
3.4%
7 22541
 
2.3%
8 19957
 
2.0%
5 17472
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 979048
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
266472
27.2%
0 207151
21.2%
2 166615
17.0%
1 161221
16.5%
6 34007
 
3.5%
9 33318
 
3.4%
3 33043
 
3.4%
7 22541
 
2.3%
8 19957
 
2.0%
5 17472
 
1.8%

ALSFC_REGIST_DE
Text

Distinct6393
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size956.2 KiB
2023-07-11T01:17:15.253656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters979048
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1653 ?
Unique (%)1.4%

Sample

1st row
2nd row20100325
3rd row
4th row20030214
5th row20070411
ValueCountFrequency (%)
20030204 68
 
0.2%
19970715 41
 
0.1%
20030210 32
 
0.1%
20030203 30
 
0.1%
20030206 30
 
0.1%
20140205 27
 
0.1%
20091127 25
 
0.1%
19891202 25
 
0.1%
20151112 24
 
0.1%
20151119 22
 
0.1%
Other values (6382) 30110
98.9%
2023-07-11T01:17:15.625580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
735576
75.1%
0 78205
 
8.0%
1 47827
 
4.9%
2 47819
 
4.9%
9 15729
 
1.6%
3 10961
 
1.1%
5 9824
 
1.0%
4 9700
 
1.0%
8 8290
 
0.8%
7 7688
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Space Separator 735576
75.1%
Decimal Number 243472
 
24.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 78205
32.1%
1 47827
19.6%
2 47819
19.6%
9 15729
 
6.5%
3 10961
 
4.5%
5 9824
 
4.0%
4 9700
 
4.0%
8 8290
 
3.4%
7 7688
 
3.2%
6 7429
 
3.1%
Space Separator
ValueCountFrequency (%)
735576
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 979048
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
735576
75.1%
0 78205
 
8.0%
1 47827
 
4.9%
2 47819
 
4.9%
9 15729
 
1.6%
3 10961
 
1.1%
5 9824
 
1.0%
4 9700
 
1.0%
8 8290
 
0.8%
7 7688
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 979048
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
735576
75.1%
0 78205
 
8.0%
1 47827
 
4.9%
2 47819
 
4.9%
9 15729
 
1.6%
3 10961
 
1.1%
5 9824
 
1.0%
4 9700
 
1.0%
8 8290
 
0.8%
7 7688
 
0.8%

COMPET_DE
Text

Distinct3897
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size956.2 KiB
2023-07-11T01:17:16.080365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters979048
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2311 ?
Unique (%)1.9%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
20140101 336
 
3.0%
20150101 319
 
2.8%
20160101 317
 
2.8%
20130101 314
 
2.8%
20120101 304
 
2.7%
20110101 244
 
2.2%
20100101 232
 
2.1%
20090101 200
 
1.8%
20080101 162
 
1.4%
20170101 146
 
1.3%
Other values (3886) 8649
77.1%
2023-07-11T01:17:16.576549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
889264
90.8%
0 30675
 
3.1%
1 22540
 
2.3%
2 16564
 
1.7%
9 4449
 
0.5%
3 3171
 
0.3%
5 2635
 
0.3%
8 2551
 
0.3%
6 2548
 
0.3%
4 2448
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Space Separator 889264
90.8%
Decimal Number 89784
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30675
34.2%
1 22540
25.1%
2 16564
18.4%
9 4449
 
5.0%
3 3171
 
3.5%
5 2635
 
2.9%
8 2551
 
2.8%
6 2548
 
2.8%
4 2448
 
2.7%
7 2203
 
2.5%
Space Separator
ValueCountFrequency (%)
889264
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 979048
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
889264
90.8%
0 30675
 
3.1%
1 22540
 
2.3%
2 16564
 
1.7%
9 4449
 
0.5%
3 3171
 
0.3%
5 2635
 
0.3%
8 2551
 
0.3%
6 2548
 
0.3%
4 2448
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 979048
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
889264
90.8%
0 30675
 
3.1%
1 22540
 
2.3%
2 16564
 
1.7%
9 4449
 
0.5%
3 3171
 
0.3%
5 2635
 
0.3%
8 2551
 
0.3%
6 2548
 
0.3%
4 2448
 
0.3%

SSS_DE
Text

Distinct318
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size956.2 KiB
2023-07-11T01:17:16.985653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters979048
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique262 ?
Unique (%)0.2%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
20210715 4
 
1.0%
20210101 4
 
1.0%
20210701 4
 
1.0%
20180501 4
 
1.0%
20130101 4
 
1.0%
20180330 4
 
1.0%
20181201 3
 
0.8%
20200323 3
 
0.8%
20190601 3
 
0.8%
20191001 3
 
0.8%
Other values (307) 359
90.9%
2023-07-11T01:17:17.477644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
975888
99.7%
0 980
 
0.1%
2 760
 
0.1%
1 711
 
0.1%
8 149
 
< 0.1%
9 122
 
< 0.1%
7 119
 
< 0.1%
3 111
 
< 0.1%
6 77
 
< 0.1%
5 71
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 975888
99.7%
Decimal Number 3160
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 980
31.0%
2 760
24.1%
1 711
22.5%
8 149
 
4.7%
9 122
 
3.9%
7 119
 
3.8%
3 111
 
3.5%
6 77
 
2.4%
5 71
 
2.2%
4 60
 
1.9%
Space Separator
ValueCountFrequency (%)
975888
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 979048
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
975888
99.7%
0 980
 
0.1%
2 760
 
0.1%
1 711
 
0.1%
8 149
 
< 0.1%
9 122
 
< 0.1%
7 119
 
< 0.1%
3 111
 
< 0.1%
6 77
 
< 0.1%
5 71
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 979048
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
975888
99.7%
0 980
 
0.1%
2 760
 
0.1%
1 711
 
0.1%
8 149
 
< 0.1%
9 122
 
< 0.1%
7 119
 
< 0.1%
3 111
 
< 0.1%
6 77
 
< 0.1%
5 71
 
< 0.1%

OPER_CLSBIZ_DE
Text

Distinct2614
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size956.2 KiB
2023-07-11T01:17:17.885190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters979048
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique749 ?
Unique (%)0.6%

Sample

1st row
2nd row
3rd row
4th row
5th row20220616
ValueCountFrequency (%)
20201202 245
 
1.0%
20211215 177
 
0.7%
20190926 145
 
0.6%
20180604 142
 
0.6%
20171030 136
 
0.6%
20190611 135
 
0.6%
20201117 135
 
0.6%
20190806 134
 
0.6%
20190613 131
 
0.5%
20200722 119
 
0.5%
Other values (2603) 22441
93.7%
2023-07-11T01:17:18.341553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
787528
80.4%
0 57514
 
5.9%
2 52448
 
5.4%
1 37448
 
3.8%
9 8162
 
0.8%
8 8017
 
0.8%
7 7453
 
0.8%
3 6945
 
0.7%
6 5472
 
0.6%
5 4261
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Space Separator 787528
80.4%
Decimal Number 191520
 
19.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 57514
30.0%
2 52448
27.4%
1 37448
19.6%
9 8162
 
4.3%
8 8017
 
4.2%
7 7453
 
3.9%
3 6945
 
3.6%
6 5472
 
2.9%
5 4261
 
2.2%
4 3800
 
2.0%
Space Separator
ValueCountFrequency (%)
787528
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 979048
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
787528
80.4%
0 57514
 
5.9%
2 52448
 
5.4%
1 37448
 
3.8%
9 8162
 
0.8%
8 8017
 
0.8%
7 7453
 
0.8%
3 6945
 
0.7%
6 5472
 
0.6%
5 4261
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 979048
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
787528
80.4%
0 57514
 
5.9%
2 52448
 
5.4%
1 37448
 
3.8%
9 8162
 
0.8%
8 8017
 
0.8%
7 7453
 
0.8%
3 6945
 
0.7%
6 5472
 
0.6%
5 4261
 
0.4%

NATION_ALSFC_AT
Text

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size956.2 KiB
2023-07-11T01:17:18.424844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters122381
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN
ValueCountFrequency (%)
n 122242
99.9%
y 139
 
0.1%
2023-07-11T01:17:18.562495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 122242
99.9%
Y 139
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 122381
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 122242
99.9%
Y 139
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 122381
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 122242
99.9%
Y 139
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 122381
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 122242
99.9%
Y 139
 
0.1%

ERDSGN_AT
Text

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size956.2 KiB
2023-07-11T01:17:18.627320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters122381
Distinct characters3
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
n 8781
92.2%
y 739
 
7.8%
2023-07-11T01:17:18.769934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
112861
92.2%
N 8781
 
7.2%
Y 739
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Space Separator 112861
92.2%
Uppercase Letter 9520
 
7.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 8781
92.2%
Y 739
 
7.8%
Space Separator
ValueCountFrequency (%)
112861
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 112861
92.2%
Latin 9520
 
7.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 8781
92.2%
Y 739
 
7.8%
Common
ValueCountFrequency (%)
112861
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 122381
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
112861
92.2%
N 8781
 
7.2%
Y 739
 
0.6%

ATNM_CHCK_TRGET_AT
Text

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size956.2 KiB
2023-07-11T01:17:18.832609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters122381
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowY
3rd rowN
4th rowY
5th rowY
ValueCountFrequency (%)
y 85964
70.2%
n 36417
29.8%
2023-07-11T01:17:18.970629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
Y 85964
70.2%
N 36417
29.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 122381
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Y 85964
70.2%
N 36417
29.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 122381
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
Y 85964
70.2%
N 36417
29.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 122381
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Y 85964
70.2%
N 36417
29.8%

DATA_ORIGIN_FLAG_CD
Text

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing53075
Missing (%)43.4%
Memory size956.2 KiB
2023-07-11T01:17:19.063476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.702522148
Min length3

Characters and Unicode

Total characters256607
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSFMS
2nd rowSFMS
3rd rowSFMS
4th rowSFMS
5th rowSFMS
ValueCountFrequency (%)
sfms 48689
70.3%
태블릿 20617
29.7%
2023-07-11T01:17:19.227000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 97378
37.9%
F 48689
19.0%
M 48689
19.0%
태 20617
 
8.0%
블 20617
 
8.0%
릿 20617
 
8.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194756
75.9%
Other Letter 61851
 
24.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 97378
50.0%
F 48689
25.0%
M 48689
25.0%
Other Letter
ValueCountFrequency (%)
태 20617
33.3%
블 20617
33.3%
릿 20617
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 194756
75.9%
Hangul 61851
 
24.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 97378
50.0%
F 48689
25.0%
M 48689
25.0%
Hangul
ValueCountFrequency (%)
태 20617
33.3%
블 20617
33.3%
릿 20617
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194756
75.9%
Hangul 61851
 
24.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 97378
50.0%
F 48689
25.0%
M 48689
25.0%
Hangul
ValueCountFrequency (%)
태 20617
33.3%
블 20617
33.3%
릿 20617
33.3%

DEL_AT
Text

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size956.2 KiB
2023-07-11T01:17:19.287436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters122381
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN
ValueCountFrequency (%)
n 114335
93.4%
y 8046
 
6.6%
2023-07-11T01:17:19.422444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 114335
93.4%
Y 8046
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 122381
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 114335
93.4%
Y 8046
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 122381
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 114335
93.4%
Y 8046
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 122381
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 114335
93.4%
Y 8046
 
6.6%

REGIST_DT
Text

Distinct1772
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size956.2 KiB
2023-07-11T01:17:19.795038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1223810
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique105 ?
Unique (%)0.1%

Sample

1st row2020-02-10
2nd row2016-12-31
3rd row2020-02-10
4th row2016-12-31
5th row2016-11-07
ValueCountFrequency (%)
2016-12-31 49839
40.7%
2016-11-07 6488
 
5.3%
2017-09-07 2526
 
2.1%
2017-11-09 1398
 
1.1%
2017-03-02 963
 
0.8%
2017-12-15 819
 
0.7%
2016-12-22 753
 
0.6%
2016-12-27 738
 
0.6%
2017-07-12 690
 
0.6%
2016-12-29 574
 
0.5%
Other values (1762) 57593
47.1%
2023-07-11T01:17:20.267959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 277284
22.7%
2 262107
21.4%
- 244762
20.0%
0 206362
16.9%
6 76047
 
6.2%
3 67324
 
5.5%
7 37410
 
3.1%
9 19658
 
1.6%
8 15044
 
1.2%
5 9189
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 979048
80.0%
Dash Punctuation 244762
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 277284
28.3%
2 262107
26.8%
0 206362
21.1%
6 76047
 
7.8%
3 67324
 
6.9%
7 37410
 
3.8%
9 19658
 
2.0%
8 15044
 
1.5%
5 9189
 
0.9%
4 8623
 
0.9%
Dash Punctuation
ValueCountFrequency (%)
- 244762
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1223810
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 277284
22.7%
2 262107
21.4%
- 244762
20.0%
0 206362
16.9%
6 76047
 
6.2%
3 67324
 
5.5%
7 37410
 
3.1%
9 19658
 
1.6%
8 15044
 
1.2%
5 9189
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1223810
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 277284
22.7%
2 262107
21.4%
- 244762
20.0%
0 206362
16.9%
6 76047
 
6.2%
3 67324
 
5.5%
7 37410
 
3.1%
9 19658
 
1.6%
8 15044
 
1.2%
5 9189
 
0.8%

UPDT_DT
Text

Distinct1842
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size956.2 KiB
2023-07-11T01:17:20.616548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1223810
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique138 ?
Unique (%)0.1%

Sample

1st row2020-02-10
2nd row2017-02-26
3rd row2021-04-29
4th row2019-03-13
5th row2022-06-17
ValueCountFrequency (%)
2020-07-27 30517
24.9%
2020-06-19 13766
 
11.2%
2016-12-31 2258
 
1.8%
2020-09-24 1081
 
0.9%
2020-06-04 883
 
0.7%
2017-11-09 480
 
0.4%
2023-03-21 451
 
0.4%
2022-08-26 391
 
0.3%
2021-10-22 357
 
0.3%
2020-07-28 345
 
0.3%
Other values (1832) 71852
58.7%
2023-07-11T01:17:21.031344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 332452
27.2%
0 311262
25.4%
- 244762
20.0%
1 126419
 
10.3%
7 75880
 
6.2%
9 33769
 
2.8%
6 30751
 
2.5%
3 29638
 
2.4%
8 15522
 
1.3%
4 12992
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 979048
80.0%
Dash Punctuation 244762
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 332452
34.0%
0 311262
31.8%
1 126419
 
12.9%
7 75880
 
7.8%
9 33769
 
3.4%
6 30751
 
3.1%
3 29638
 
3.0%
8 15522
 
1.6%
4 12992
 
1.3%
5 10363
 
1.1%
Dash Punctuation
ValueCountFrequency (%)
- 244762
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1223810
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 332452
27.2%
0 311262
25.4%
- 244762
20.0%
1 126419
 
10.3%
7 75880
 
6.2%
9 33769
 
2.8%
6 30751
 
2.5%
3 29638
 
2.4%
8 15522
 
1.3%
4 12992
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1223810
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 332452
27.2%
0 311262
25.4%
- 244762
20.0%
1 126419
 
10.3%
7 75880
 
6.2%
9 33769
 
2.8%
6 30751
 
2.5%
3 29638
 
2.4%
8 15522
 
1.3%
4 12992
 
1.1%